Next Article in Journal
Assessment of the Phytochemical Profile, Antioxidant Capacity, and Hepatoprotective Effect of Andrographis paniculata against CCl4-Induced Liver Dysfunction in Wistar Albino Rats
Previous Article in Journal
Epidemiology and Trends in Cartilage Surgery of the Foot and Ankle in Germany: An Analysis of National Healthcare Billing and Reporting Data from 2006 to 2020
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 59
Issue 7
10.3390/medicina59071258
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Significance of Tumor–Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors

by
Jung-Soo Pyo
1,†,
Nae Yu Kim
2,†,
Kyueng-Whan Min
1 and
Dong-Wook Kang
3,4,*
1
Department of Pathology, Uijeongbu Eulji University Hospital, Eulji University School of Medicine, Uijeongbu-si 11759, Republic of Korea
2
Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu-si 11759, Republic of Korea
3
Department of Pathology, Chungnam National University Sejong Hospital, 20 Bodeum 7-ro, Sejong 30099, Republic of Korea
4
Department of Pathology, Chungnam National University School of Medicine, 266 Munhwa Street, Daejeon 35015, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Medicina 2023, 59(7), 1258; https://doi.org/10.3390/medicina59071258
Submission received: 4 May 2023 / Revised: 17 June 2023 / Accepted: 27 June 2023 / Published: 6 July 2023
Browse Figure
Review Reports Versions Notes

Abstract

:
Background and Objectives: The present study aimed to elucidate the distribution and the prognostic implications of tumor–stroma ratio (TSR) in various malignant tumors through a meta-analysis. Materials and Methods: This meta-analysis included 51 eligible studies with information for overall survival (OS) or disease-free survival (DFS), according to TSR. In addition, subgroup analysis was performed based on criteria for high TSR. Results: The estimated rate of high TSR was 0.605 (95% confidence interval (CI) 0.565–0.644) in overall malignant tumors. The rates of high TSR ranged from 0.276 to 0.865. The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827–0.895). The estimated high TSR rates of colorectal, esophageal, and stomach cancers were 0.622, 0.529, and 0.448, respectively. In overall cases, patients with high TSR had better OS and DFS than those with low TSR (hazard ratio (HR) 0.631, 95% CI 0.542–0.734, and HR 0.564, 95% CI 0.0.476–0.669, respectively). Significant correlations with OS were found in the breast, cervical, colorectal, esophagus, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, larynx, lung, and stomach cancers. In endometrial cancers, high TSR was significantly correlated with worse OS and DFS. Conclusions: The rate of high TSR was different in various malignant tumors. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors.

1. Introduction

Pathological examination is performed through the interpretation of glass slides with hematoxylin and eosin (H&E) staining. In the pathological assessment of malignant tumors, the primary tumor, regional lymph node, and distant metastasis are evaluated according to the American Joint Committee on Cancer (AJCC) Cancer Staging Manual [1]. Tumor differentiation, lymphovascular invasion, perineural invasion, and resection margin involvement are evaluated in daily practice. Useful parameters for predicting the patient’s prognosis should have easy identification, high reproducibility, and less discrepancy between investigators. An epithelial tumor is composed of the tumor and surrounding stroma. Interaction between tumor cells and intra- and peritumoral stroma is important in tumor progression [2]. Evaluating these interactions can be useful for understanding tumor behavior. Stroma includes various components, such as immune cells, fibroblasts, and the extracellular matrix [3,4,5,6]. The tumor–stroma ratio (TSR), defined as the proportion of tumor area in the overall tumor, has been studied as a histologic assessment [2,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]. TSR is assessed by microscopic observation with H&E staining and is a method that can be sufficiently evaluated in routine pathology laboratories. However, the impact of the proportion of stroma is not clear in terms of whether it accelerates or suppresses tumor progression. Recently, the prognostic implications of TSR have been exhibited for various malignant tumors [2,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57]. In colorectal cancers, low stroma was associated with less frequent vascular and perineural invasion and distant metastasis [45]. HIF-1α was found to be highly expressed in stroma-high tumors, with correspondingly high microvessel density in colorectal cancers. There is no conclusive information on the prognostic impacts of TSR in various malignant tumors. TSR is divided into high TSR (stroma-low) and low TSR (stroma-high) by evaluation criteria. In previous studies, the evaluation criteria of TSR affected high TSR rates [2,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57]. If the evaluation criteria are different, the prognostic implications of TSR can differ. The evaluations of TSR have been shown to have good interobserver agreement [58] but may be more influenced by criteria. In addition, there are carcinomas that require more careful evaluation, such as lung cancer, where the amount of stroma can be inherently different between histological subtypes. It is difficult to evaluate the implications of TSR from individual studies. It is important to determine the direction of further research and analysis from a comprehensive analysis. A meta-analysis study using previous literature can be useful in obtaining comprehensive information.
We investigated high TSR rates of various malignant tumors according to malignant tumor evaluation criteria. The correlations between TSR and survival were elucidated through the subgroup analysis based on malignant tumors. The high TSR rates and prognostic impact of TSR according to evaluation criteria were analyzed.

2. Materials and Methods

2.1. Published Study Search and Selection Criteria

Relevant articles were obtained by searching the PubMed database through 15 February 2023. The following keywords were used in the search: “(tumor–stroma ratio or carcinoma–stroma ratio) AND (cancer or tumor or malignancy or neoplasm or carcinoma) AND (prognosis or prognostic or survival)”. The titles and abstracts of all searched articles were screened for inclusion and exclusion. Included articles had information on the correlation between TSR and survival in malignant tumors. However, non-original articles, such as case reports and review articles, were excluded. Articles not written in English were not included in the present study. Finally, 51 eligible articles were included in the meta-analysis (Table 1). The PRISMA checklist is shown in Supplementary Table S1. In addition, we evaluated eligible studies using the Newcastle–Ottawa Scale, and the results are presented in Table 2.

2.2. Data Extraction

All data were extracted from 51 eligible studies [2,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57]. Extracted data included the author’s information, study location, number of patients analyzed, and high TSR evaluation criteria. The number and survival rates of high and low TSR were also investigated. For the quantitative aggregation of the survival results, the correlation between TSR and survival was analyzed according to the hazard ratio (HR) using one of three methods. In studies that did not take note of HRs or confidence intervals (CIs), these variables were calculated from the presented data using the HR point estimate, the log-rank statistic or its p-value, and the O-E statistic (the difference between the number of observed and expected events) or its variance. If those data were unavailable, HR was estimated using the total number of events, the number of patients at risk in each group, and the log-rank statistic or its p-value. Finally, if the only useful data were in the form of graphical representations of survival distributions, survival rates were extracted at specified times to reconstruct the HR estimate and its variance under the assumption that patients were censored at a constant rate during the time intervals [59]. The published survival curves were read independently by two authors to reduce reading variability. The HRs were then combined into an overall HR using Peto’s method [60]. Two independent authors obtained all data (Pyo J.S. and Kim N.Y.).

2.3. Statistical Analyses

The meta-analysis was performed using the Comprehensive Meta-Analysis software package (Biostat, Englewood, NJ, USA). The high TSR rate was investigated in various malignant tumors. TSR’s prognostic impact was evaluated, dividing survival into overall survival (OS) and disease-free survival (DFS). Heterogeneity between the studies was checked by the Q and I2 statistics and expressed as p-values. Additionally, sensitivity analysis was conducted to assess the heterogeneity of eligible studies and each study’s impact on the combined effects. In the meta-analysis, because the eligible studies used various malignant tumors and populations, a random-effects model rather than a fixed-effects model was more suitable. Begg’s funnel plot and Egger’s test were used; if significant publication bias was found, the fail-safe N and trim–fill tests were also used to confirm the degree of publication bias. The results were considered statistically significant at p < 0.05.

3. Results

3.1. Selection and Characteristics of the Studies

  • A primary search using the PubMed database found 509 relevant articles. In screening and reviewing, 409 were excluded due to inapplicable or insufficient information. Among the remaining articles, 49 reports were excluded for the following reasons: non-original articles (n = 31), non-human studies (n = 5), a language other than English (n = 11), and articles including duplicated patients (n = 2) (Figure 1).

3.2. Prevalence of High Tumor–Stroma Ratio

The estimated high TSR rate was 0.605 (95% CI 0.565–0.644) in overall tumors (Table 3). The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827–0.895). Other female genital tract cancers, the cervical and ovary cancers, showed 0.785 (95% CI 0.713–0.842) and 0.601 (95% CI 0.417–0.761), respectively. The estimated rates of colorectal, esophageal, and stomach cancers were 0.622 (95% CI 0.556–0.683), 0.529 (95% CI 0.312–0.736), and 0.448 (95% CI 0.387–0.509), respectively. Breast cancers showed a high TSR of 50.1%. Next, subgroup analysis based on criteria for high TSR was performed because eligible studies used various criteria for high TSR. The criteria ranged from 30% to 70%. The high TSR rates for <50%, 50%, and >50% cut-off subgroups were 0.624 (95% CI 0.515–0.721), 0.609 (95% CI 0.567–0.649), and 0.399 (95% CI 0.302–0.506), respectively.

3.3. Correlation between High Tumor–Stroma Ratio and Survival

In overall cases, high TSR was significantly correlated with better OS and DFS compared to low TSR (HR 0.631, 95% CI 0.542–0.734, and HR 0.564, 95% CI 0.476–0.669, respectively; Table 4 and Table 5). Significant correlations with OS were found in breast, cervical, colorectal, esophageal, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, laryngeal, lung, and stomach cancers. However, in endometrial and pancreas cancers, high TSR was significantly correlated with a worse prognosis. In subgroup analysis based on evaluation criteria, there were significant correlations between high TSR and better OS and DFS in the subgroups with criteria <50% and 50%. In the subgroup with criteria >50%, patients with high TSR had a better OS, but not DFS, compared to patients with low TSR.

4. Discussion

In the present meta-analysis, the rates of high TSR were evaluated in various malignant tumors. In addition, the correlation between TSR and survival was investigated through a meta-analysis. Previous studies used variable methods for evaluating TSR. The criterion for high TSR is usually 50% through visual inspection. Therefore, a meta-analysis is more useful for understanding the prognostic implication of TSR. The present study is the first meta-analysis, to the best of our knowledge, to elucidate the prognostic impacts of TSR according to malignant tumors and evaluation criteria.
Regardless of the origin of epithelial tumors, malignant tumors initiate through invasion into the basement membrane and progress to the stroma. This process induces changes in the characteristics of the stroma, including fibroblast proliferation and extracellular matrix deposition, through the production of cytokines and enzymes with the surrounding stroma [61,62,63,64]. Therefore, in malignant tumors, the interaction between tumor cells and stroma is important [33]. Malignant tumors have intratumoral stroma and an interface with peritumoral stroma. The definition of TSR is the proportion of tumor area in the overall tumor, including the stroma. Tumors with low TSR, which have abundant stroma, are considered active interactions between tumor cells and stroma. The tumor growth and progression are associated with the tumor microenvironment [65]. However, the detailed evaluation of the tumor environment through hematoxylin and eosin staining can be limited. TSR can be considered as a simplified analysis of the interaction between tumor cells and stroma. Therefore, the assessment of TSR may be applicable for predicting the prognosis through the routine evaluation of histology.
Recently, assessments using image analyzers have been increasingly used in research and practice. Evaluation of TSR is performed through various methods, including eyeballing and the use of a digital image analyzer [15]. The assessment of TSR can be affected by multiple factors, including the discrepancy between investigators. The evaluation criteria for high TSR are yet to be elucidated. To diminish the discrepancy caused by various factors, an image analyzer is used for evaluating TSR. The value of TSR can be different according to the evaluation foci within the tumor. The evaluation area can also affect the value of TSR, and two-tier or three-tier classification can affect the prognostic impact of TSR. Further cumulative studies for the prognostic implication of TSR gradients by evaluation criteria will be needed.
Most eligible studies investigated the evaluation criterion of 50% for high TSR. However, the previous meta-analysis showed no results for evaluation criteria [66]. With the increasing cut-off for high TSR, the rate of high TSR is lowering. The rates of high TSR were 0.624, 0.609, and 0.399 in the <50%, 50%, and >50% cut-off subgroups, respectively. In the present study, patients with high TSR had better OS and DFS than those with low TSR in the <50% and 50% cut-off subgroups. However, in the subgroup with criteria >50%, patients with high TSR had a better OS, but not DFS. In the assessment of OS in criteria >50%, colorectal cancers are only included. However, in the assessment of DFS in criteria >50%, one breast cancer study and one colorectal cancer study were included. Among these studies, there was no significant correlation between high TSR and better prognosis in a study on only breast cancer [50]. In subgroup analysis, breast cancers showed a significant correlation between high TSR and better DFS (Table 5). Although there may be a difference in the degree of HR, it can be considered that there is no significant difference in the relationship with prognosis according to the criteria.
In a pathological examination, the evaluation criteria of TNM staging differ according to malignant tumors. For example, in lung cancers, the pT stage is evaluated by tumor size and invasion depth [1]. The invasive size, rather than the overall tumor size, was significantly correlated with lung adenocarcinoma [67,68]. In addition, lung adenocarcinoma includes various histologic subtypes, such as lepidic, acinar, micropapillary, papillary, and solid adenocarcinomas. Although these subtypes have variable amounts of stroma, the specific correlation between histologic subtypes and stroma amount is not clear. Evaluating the TSR of lepidic adenocarcinoma, which has similarities with the lung’s normal parenchyma, may not be easy. Ichikawa et al. reported that lung adenocarcinoma with low TSR was significantly correlated with favorable tumor behaviors [19]. However, Xi et al. reported a significant correlation between low TSR and worse prognosis [33]. Xi’s report included adenocarcinoma and squamous cell carcinoma. However, the prognostic impact based on histologic subtypes of non-small cell lung cancers could not be elucidated in that study. In a previous study, TSR was not correlated with various clinicopathological characteristics, including histologic subtypes, pT stage, pN stage, and pTNM stage [33].
A previous meta-analysis was reported for the prognostic roles of TSR in gastrointestinal tract cancers [66]. There were significant correlations between high TSR and better OS in colorectal, stomach, and liver cancers. However, some discrepancies are present compared to our results. In the current meta-analysis, there was no significant correlation between TSR and OS in liver cancer. The highest and lowest rates of high TSR were found in endometrial cancer (86.5%) and stomach cancer (44.8%), respectively. This discrepancy can be caused by different characteristics of malignant tumors. Endometrial intraepithelial neoplasm, which is a precursor of endometrial cancer, has less stroma compared to the tumor area. Interestingly, for endometrial carcinoma, it was shown that high TSR was significantly correlated with worse OS and DFS. However, cervical cancers showed a significant correlation between TSR and better OS and DFS. In addition, high TSR of ovarian cancers was significantly correlated with better OS. These results suggest that the biology of tumor–stroma interactions may differ amongst cancer types.
There were some limitations in the current meta-analysis. First, high TSR rates based on histologic subtypes of each tumor could not be investigated due to insufficient information. Second, each study was not described for the evaluation area or section. In addition, it is uncertain whether the evaluation foci for TSR are hot spots or representative regions. Third, a comparison between eyeballing and image analyzers could not be performed due to insufficient information on eligible studies. Fourth, we were unable to conduct analyses by criteria subgroup for high TSR for each cancer type due to insufficient information.

5. Conclusions

In conclusion, our results showed that high TSR rates were different between malignant tumors. High TSR was significantly correlated with better survival rates, although some malignant tumors had no correlation or opposite correlation. Our results show that endometrial and pancreatic cancers are correlated with a poor prognosis. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors. Further studies for standardized histopathologic criteria will be needed in the application of TSR.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/medicina59071258/s1, Table S1. PRISMA checklist.

Author Contributions

Conceptualization, J.-S.P. and N.Y.K.; methodology, J.-S.P.; software, J.-S.P.; data curation, K.-W.M.; writing—original draft preparation, J.-S.P. and N.Y.K.; writing—review and editing, D.-W.K.; funding acquisition, D.-W.K. All authors have read and agreed to the published version of the manuscript.

Funding

The article was supported by the research fund of the Catholic University of Korea, Industry-Academic Cooperation Foundation (2022-81-001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Amin, M.B.; Greene, F.L.; Edge, S.B.; Compton, C.C.; Gershenwald, J.E.; Brookland, R.K.; Meyer, L.; Gress, D.M.; Byrd, D.R.; Winchester, D.P. The AJCC Cancer Staging Manual, 8th ed.; Springer: New York, NY, USA, 2016. [Google Scholar]
  2. Karpathiou, G.; Vieville, M.; Gavid, M.; Camy, F.; Dumollard, J.M.; Magné, N.; Froudarakis, M.; Prades, J.M.; Peoc’h, M. Prognostic significance of tumor budding, tumor-stroma ratio, cell nests size, and stroma type in laryngeal and pharyngeal squamous cell carcinomas. Head Neck 2019, 41, 1918–1927. [Google Scholar] [CrossRef] [PubMed]
  3. Bissell, M.J.; Radisky, D. Putting tumours in context. Nat. Rev. Cancer 2001, 1, 46–54. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Bremnes, R.M.; Dønnem, T.; Al-Saad, S.; Al-Shibli, K.; Andersen, S.; Sirera, R.; Camps, C.; Marinez, I.; Busund, L.T. The role of tumor stroma in cancer progression and prognosis: Emphasis on carcinoma-associated fibroblasts and non-small cell lung cancer. J. Thorac. Oncol. Off. Publ. Int. Assoc. Study Lung Cancer 2011, 6, 209–217. [Google Scholar] [CrossRef] [Green Version]
  5. Neri, S.; Ishii, G.; Hashimoto, H.; Kuwata, T.; Nagai, K.; Date, H.; Ochiai, A. Podoplanin-expressing cancer-associated fibroblasts lead and enhance the local invasion of cancer cells in lung adenocarcinoma. Int. J. Cancer 2015, 137, 784–796. [Google Scholar] [CrossRef]
  6. Ishii, G.; Ochiai, A.; Neri, S. Phenotypic and functional heterogeneity of cancer-associated fibroblast within the tumor microenvironment. Adv. Drug Deliv. Rev. 2016, 99, 186–196. [Google Scholar] [CrossRef]
  7. Travis, W.D.; Asamura, H.; Bankier, A.A.; Beasley, M.B.; Detterbeck, F.; Flieder, D.B.; Goo, J.M.; MacMahon, H.; Naidich, D.; Nicholson, A.G.; et al. The IASLC Lung Cancer Staging Project: Proposals for Coding T Categories for Subsolid Nodules and Assessment of Tumor Size in Part-Solid Tumors in the Forthcoming Eighth Edition of the TNM Classification of Lung Cancer. J. Thorac. Oncol. Off. Publ. Int. Assoc. Study Lung Cancer 2016, 11, 1204–1223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Almangush, A.; Heikkinen, I.; Bakhti, N.; Mäkinen, L.K.; Kauppila, J.H.; Pukkila, M.; Hagström, J.; Laranne, J.; Soini, Y.; Kowalski, L.P.; et al. Prognostic impact of tumour-stroma ratio in early-stage oral tongue cancers. Histopathology 2018, 72, 1128–1135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Aurello, P.; Berardi, G.; Giulitti, D.; Palumbo, A.; Tierno, S.M.; Nigri, G.; D’Angelo, F.; Pilozzi, E.; Ramacciato, G. Tumor-Stroma Ratio is an independent predictor for overall survival and disease free survival in gastric cancer patients. Surg. J. R. Coll. Surg. Edinb. Irel. 2017, 15, 329–335. [Google Scholar] [CrossRef]
  10. Chen, Y.; Zhang, L.; Liu, W.; Liu, X. Prognostic Significance of the Tumor-Stroma Ratio in Epithelial Ovarian Cancer. BioMed Res. Int. 2015, 2015, 589301. [Google Scholar] [CrossRef] [Green Version]
  11. Courrech Staal, E.F.; Wouters, M.W.; van Sandick, J.W.; Takkenberg, M.M.; Smit, V.T.; Junggeburt, J.M.; Spitzer-Naaykens, J.M.; Karsten, T.; Hartgrink, H.H.; Mesker, W.E.; et al. The stromal part of adenocarcinomas of the oesophagus: Does it conceal targets for therapy? Eur. J. Cancer 2010, 46, 720–728. [Google Scholar] [CrossRef]
  12. de Kruijf, E.M.; van Nes, J.G.; van de Velde, C.J.; Putter, H.; Smit, V.T.; Liefers, G.J.; Kuppen, P.J.; Tollenaar, R.A.; Mesker, W.E. Tumor-stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients. Breast Cancer Res. Treat. 2011, 125, 687–696. [Google Scholar] [CrossRef]
  13. Dekker, T.J.; van de Velde, C.J.; van Pelt, G.W.; Kroep, J.R.; Julien, J.P.; Smit, V.T.; Tollenaar, R.A.; Mesker, W.E. Prognostic significance of the tumor-stroma ratio: Validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854). Breast Cancer Res. Treat. 2013, 139, 371–379. [Google Scholar] [CrossRef]
  14. Dourado, M.R.; Miwa, K.Y.M.; Hamada, G.B.; Paranaíba, L.M.R.; Sawazaki-Calone, Í.; Domingueti, C.B.; Ervolino de Oliveira, C.; Furlan, E.C.B.; Longo, B.C.; Almangush, A.; et al. Prognostication for oral squamous cell carcinoma patients based on the tumour-stroma ratio and tumour budding. Histopathology 2020, 76, 906–918. [Google Scholar] [CrossRef]
  15. Geessink, O.G.F.; Baidoshvili, A.; Klaase, J.M.; Ehteshami Bejnordi, B.; Litjens, G.J.S.; van Pelt, G.W.; Mesker, W.E.; Nagtegaal, I.D.; Ciompi, F.; van der Laak, J. Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer. Cell. Oncol. 2019, 42, 331–341. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Hansen, T.F.; Kjær-Frifeldt, S.; Lindebjerg, J.; Rafaelsen, S.R.; Jensen, L.H.; Jakobsen, A.; Sørensen, F.B. Tumor-stroma ratio predicts recurrence in patients with colon cancer treated with neoadjuvant chemotherapy. Acta Oncol. 2018, 57, 528–533. [Google Scholar] [CrossRef] [PubMed]
  17. Huijbers, A.; Tollenaar, R.A.; v Pelt, G.W.; Zeestraten, E.C.; Dutton, S.; McConkey, C.C.; Domingo, E.; Smit, V.T.; Midgley, R.; Warren, B.F.; et al. The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: Validation in the VICTOR trial. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2013, 24, 179–185. [Google Scholar] [CrossRef] [PubMed]
  18. Huijbers, A.; van Pelt, G.W.; Kerr, R.S.; Johnstone, E.C.; Tollenaar, R.; Kerr, D.J.; Mesker, W.E. The value of additional bevacizumab in patients with high-risk stroma-high colon cancer. A study within the QUASAR2 trial, an open-label randomized phase 3 trial. J. Surg. Oncol. 2018, 117, 1043–1048. [Google Scholar] [CrossRef]
  19. Ichikawa, T.; Aokage, K.; Sugano, M.; Miyoshi, T.; Kojima, M.; Fujii, S.; Kuwata, T.; Ochiai, A.; Suzuki, K.; Tsuboi, M.; et al. The ratio of cancer cells to stroma within the invasive area is a histologic prognostic parameter of lung adenocarcinoma. Lung Cancer 2018, 118, 30–35. [Google Scholar] [CrossRef]
  20. Kairaluoma, V.; Kemi, N.; Pohjanen, V.M.; Saarnio, J.; Helminen, O. Tumour budding and tumour-stroma ratio in hepatocellular carcinoma. Br. J. Cancer 2020, 123, 38–45. [Google Scholar] [CrossRef]
  21. Kemi, N.; Eskuri, M.; Herva, A.; Leppänen, J.; Huhta, H.; Helminen, O.; Saarnio, J.; Karttunen, T.J.; Kauppila, J.H. Tumour-stroma ratio and prognosis in gastric adenocarcinoma. Br. J. Cancer 2018, 119, 435–439. [Google Scholar] [CrossRef] [Green Version]
  22. Labiche, A.; Heutte, N.; Herlin, P.; Chasle, J.; Gauduchon, P.; Elie, N. Stromal compartment as a survival prognostic factor in advanced ovarian carcinoma. Int. J. Gynecol. Cancer Off. J. Int. Gynecol. Cancer Soc. 2010, 20, 28–33. [Google Scholar] [CrossRef] [PubMed]
  23. Li, H.; Yuan, S.L.; Han, Z.Z.; Huang, J.; Cui, L.; Jiang, C.Q.; Zhang, Y. Prognostic significance of the tumor-stroma ratio in gallbladder cancer. Neoplasma 2017, 64, 588–593. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, J.; Liu, J.; Li, J.; Chen, Y.; Guan, X.; Wu, X.; Hao, C.; Sun, Y.; Wang, Y.; Wang, X. Tumor-stroma ratio is an independent predictor for survival in early cervical carcinoma. Gynecol. Oncol. 2014, 132, 81–86. [Google Scholar] [CrossRef] [PubMed]
  25. Lv, Z.; Cai, X.; Weng, X.; Xiao, H.; Du, C.; Cheng, J.; Zhou, L.; Xie, H.; Sun, K.; Wu, J.; et al. Tumor-stroma ratio is a prognostic factor for survival in hepatocellular carcinoma patients after liver resection or transplantation. Surgery 2015, 158, 142–150. [Google Scholar] [CrossRef]
  26. Mascitti, M.; Zhurakivska, K.; Togni, L.; Caponio, V.C.A.; Almangush, A.; Balercia, P.; Balercia, A.; Rubini, C.; Lo Muzio, L.; Santarelli, A.; et al. Addition of the tumour-stroma ratio to the 8th edition American Joint Committee on Cancer staging system improves survival prediction for patients with oral tongue squamous cell carcinoma. Histopathology 2020, 77, 810–822. [Google Scholar] [CrossRef]
  27. Panayiotou, H.; Orsi, N.M.; Thygesen, H.H.; Wright, A.I.; Winder, M.; Hutson, R.; Cummings, M. The prognostic significance of tumour-stroma ratio in endometrial carcinoma. BMC Cancer 2015, 15, 955. [Google Scholar] [CrossRef] [Green Version]
  28. Peng, C.; Liu, J.; Yang, G.; Li, Y. The tumor-stromal ratio as a strong prognosticator for advanced gastric cancer patients: Proposal of a new TSNM staging system. J. Gastroenterol. 2018, 53, 606–617. [Google Scholar] [CrossRef] [Green Version]
  29. Pongsuvareeyakul, T.; Khunamornpong, S.; Settakorn, J.; Sukpan, K.; Suprasert, P.; Intaraphet, S.; Siriaunkgul, S. Prognostic evaluation of tumor-stroma ratio in patients with early stage cervical adenocarcinoma treated by surgery. Asian Pac. J. Cancer Prev. 2015, 16, 4363–4368. [Google Scholar] [CrossRef] [Green Version]
  30. Sandberg, T.P.; Oosting, J.; van Pelt, G.W.; Mesker, W.E.; Tollenaar, R.; Morreau, H. Molecular profiling of colorectal tumors stratified by the histological tumor-stroma ratio—Increased expression of galectin-1 in tumors with high stromal content. Oncotarget 2018, 9, 31502–31515. [Google Scholar] [CrossRef] [Green Version]
  31. Scheer, R.; Baidoshvili, A.; Zoidze, S.; Elferink, M.A.G.; Berkel, A.E.M.; Klaase, J.M.; van Diest, P.J. Tumor-stroma ratio as prognostic factor for survival in rectal adenocarcinoma: A retrospective cohort study. World J. Gastrointest. Oncol. 2017, 9, 466–474. [Google Scholar] [CrossRef]
  32. Vogelaar, F.J.; van Pelt, G.W.; van Leeuwen, A.M.; Willems, J.M.; Tollenaar, R.A.; Liefers, G.J.; Mesker, W.E. Are disseminated tumor cells in bone marrow and tumor-stroma ratio clinically applicable for patients undergoing surgical resection of primary colorectal cancer? The Leiden MRD study. Cell. Oncol. 2016, 39, 537–544. [Google Scholar] [CrossRef] [Green Version]
  33. Xi, K.X.; Wen, Y.S.; Zhu, C.M.; Yu, X.Y.; Qin, R.Q.; Zhang, X.W.; Lin, Y.B.; Rong, T.H.; Wang, W.D.; Chen, Y.Q.; et al. Tumor-stroma ratio (TSR) in non-small cell lung cancer (NSCLC) patients after lung resection is a prognostic factor for survival. J. Thorac. Dis. 2017, 9, 4017–4026. [Google Scholar] [CrossRef] [Green Version]
  34. Xu, Q.; Yuan, J.P.; Chen, Y.Y.; Zhang, H.Y.; Wang, L.W.; Xiong, B. Prognostic Significance of the Tumor-Stromal Ratio in Invasive Breast Cancer and a Proposal of a New Ts-TNM Staging System. J. Oncol. 2020, 2020, 9050631. [Google Scholar] [CrossRef]
  35. Zengin, M. Tumour Budding and Tumour Stroma Ratio are Reliable Predictors for Death and Recurrence in Elderly Stage I Colon Cancer Patients. Pathol. Res. Pract. 2019, 215, 152635. [Google Scholar] [CrossRef]
  36. Zhang, X.L.; Jiang, C.; Zhang, Z.X.; Liu, F.; Zhang, F.; Cheng, Y.F. The tumor-stroma ratio is an independent predictor for survival in nasopharyngeal cancer. Oncol. Res. Treat. 2014, 37, 480–484. [Google Scholar] [CrossRef]
  37. Zhang, T.; Xu, J.; Shen, H.; Dong, W.; Ni, Y.; Du, J. Tumor-stroma ratio is an independent predictor for survival in NSCLC. Int. J. Clin. Exp. Pathol. 2015, 8, 11348–11355. [Google Scholar]
  38. Zong, L.; Zhang, Q.; Kong, Y.; Yang, F.; Zhou, Y.; Yu, S.; Wu, M.; Chen, J.; Zhang, Y.; Xiang, Y. The tumor-stroma ratio is an independent predictor of survival in patients with 2018 FIGO stage IIIC squamous cell carcinoma of the cervix following primary radical surgery. Gynecol. Oncol. 2020, 156, 676–681. [Google Scholar] [CrossRef]
  39. Aboelnasr, L.S.; El-Rebey, H.S.; Mohamed, A.; Abdou, A.G. The Prognostic Impact of Tumor Border Configuration, Tumor Budding and Tumor Stroma Ratio in Colorectal Carcinoma. Turk. Patoloji Derg. 2023, 39, 83–93. [Google Scholar] [CrossRef] [PubMed]
  40. Alessandrini, L.; Ferrari, M.; Taboni, S.; Sbaraglia, M.; Franz, L.; Saccardo, T.; Del Forno, B.M.; Agugiaro, F.; Frigo, A.C.; Dei Tos, A.P.; et al. Tumor-stroma ratio, neoangiogenesis and prognosis in laryngeal carcinoma. A pilot study on preoperative biopsies and matched surgical specimens. Oral Oncol. 2022, 132, 105982. [Google Scholar] [CrossRef] [PubMed]
  41. Goyal, S.; Banga, P.; Meena, N.; Chauhan, G.; Sakhuja, P.; Agarwal, A.K. Prognostic significance of tumour budding, tumour-stroma ratio and desmoplastic stromal reaction in gall bladder carcinoma. J. Clin. Pathol. 2021, 76, 308–314. [Google Scholar] [CrossRef] [PubMed]
  42. He, R.; Li, D.; Liu, B.; Rao, J.; Meng, H.; Lin, W.; Fan, T.; Hao, B.; Zhang, L.; Lu, Z.; et al. The prognostic value of tumor-stromal ratio combined with TNM staging system in esophagus squamous cell carcinoma. J. Cancer 2021, 12, 1105–1114. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, S.; Cai, H.; Song, F.; Zhu, Y.; Hou, C.; Hou, J. Tumor-stroma ratio is a crucial histological predictor of occult cervical lymph node metastasis and survival in early-stage (cT1/2N0) oral squamous cell carcinoma. Int. J. Oral Maxillofac Surg. 2022, 51, 450–458. [Google Scholar] [CrossRef] [PubMed]
  44. Inoue, H.; Kudou, M.; Shiozaki, A.; Kosuga, T.; Shimizu, H.; Kiuchi, J.; Arita, T.; Konishi, H.; Komatsu, S.; Kuriu, Y.; et al. Value of the Tumor Stroma Ratio and Structural Heterogeneity Measured by a Novel Semi-Automatic Image Analysis Technique for Predicting Survival in Patients with Colon Cancer. Dis. Colon Rectum 2022. [Google Scholar] [CrossRef] [PubMed]
  45. Kang, G.; Pyo, J.S.; Kim, N.Y.; Kang, D.W. Clinicopathological Significances of Tumor-Stroma Ratio (TSR) in Colorectal Cancers: Prognostic Implication of TSR Compared to Hypoxia-Inducible Factor-1α Expression and Microvessel Density. Curr. Oncol. 2021, 28, 1314–1324. [Google Scholar] [CrossRef]
  46. Kang, J.; Su, M.; Xu, Q.; Wang, C.; Yuan, X.; Han, Z. Tumour-stroma ratio is a valuable prognostic factor for oral tongue squamous cell carcinoma. Oral Dis. 2023, 29, 628–638. [Google Scholar] [CrossRef]
  47. Kim, E.Y.; Abdul-Ghafar, J.; Chong, Y.; Yim, K. Calculated Tumor-Associated Neutrophils Are Associated with the Tumor-Stroma Ratio and Predict a Poor Prognosis in Advanced Gastric Cancer. Biomedicines 2022, 10, 708. [Google Scholar] [CrossRef]
  48. Li, B.; Wang, Y.; Jiang, H.; Li, B.; Shi, X.; Gao, S.; Ni, C.; Zhang, Z.; Guo, S.; Xu, J.; et al. Pros and Cons: High Proportion of Stromal Component Indicates Better Prognosis in Patients With Pancreatic Ductal Adenocarcinoma-A Research Based on the Evaluation of Whole-Mount Histological Slides. Front. Oncol. 2020, 10, 1472. [Google Scholar] [CrossRef]
  49. Öztürk, Ç.; Okcu, O.; Şen, B.; Bedir, R. An easy and practical prognostic parameter: Tumor-stroma ratio in Luminal, Her2, and triple-negative breast cancers. Rev. Assoc. Med. Bras. 2022, 68, 227–233. [Google Scholar] [CrossRef]
  50. Qian, X.; Xiao, F.; Chen, Y.Y.; Yuan, J.P.; Liu, X.H.; Wang, L.W.; Xiong, B. Computerized Assessment of the Tumor-stromal Ratio and Proposal of a Novel Nomogram for Predicting Survival in Invasive Breast Cancer. J. Cancer 2021, 12, 3427–3438. [Google Scholar] [CrossRef]
  51. Qiu, J.; Jiang, E.; Shang, Z. Prognostic value of tumor-stroma ratio in oral carcinoma: Role of cancer-associated fibroblasts. Oral Dis. 2022, 29, 1967–1978. [Google Scholar] [CrossRef]
  52. Silva, G.V.D.; da Silva Dolens, E.; Paranaíba, L.M.R.; Ayroza, A.L.C.; Gurgel Rocha, C.A.; Almangush, A.; Salo, T.; Brennan, P.A.; Coletta, R.D. Exploring the combination of tumor-stroma ratio, tumor-infiltrating lymphocytes, and tumor budding with WHO histopathological grading on early-stage oral squamous cell carcinoma prognosis. J. Oral Pathol. Med. 2022, 52, 402–409. [Google Scholar] [CrossRef]
  53. Smit, M.A.; Philipsen, M.W.; Postmus, P.E.; Putter, H.; Tollenaar, R.A.; Cohen, D.; Mesker, W.E. The prognostic value of the tumor-stroma ratio in squamous cell lung cancer, a cohort study. Cancer Treat. Res. Commun. 2020, 25, 100247. [Google Scholar] [CrossRef]
  54. Uzun, M.A.; Tilki, M.; Gönültaş, A.; Aker, F.; Kayaoglu, S.A.; Okuyan, G. Is the tumor-stroma ratio a prognostic factor in gallbladder cancer? Rev. Assoc. Med. Bras. 2022, 68, 664–669. [Google Scholar] [CrossRef]
  55. Xu, L.; Zhong, W.; Li, C.; Hong, P.; Xia, K.; Lin, R.; Cheng, S.; Wang, B.; Yang, M.; Chen, J.; et al. The tumour-associated stroma correlates with poor clinical outcomes and immunoevasive contexture in patients with upper tract urothelial carcinoma: Results from a multicenter real-world study (TSU-01 Study). Br. J. Cancer 2023, 128, 310–320. [Google Scholar] [CrossRef] [PubMed]
  56. Yan, D.; Ju, X.; Luo, B.; Guan, F.; He, H.; Yan, H.; Yuan, J. Tumour stroma ratio is a potential predictor for 5-year disease-free survival in breast cancer. BMC Cancer 2022, 22, 1082. [Google Scholar] [CrossRef]
  57. Zheng, Q.; Jiang, Z.; Ni, X.; Yang, S.; Jiao, P.; Wu, J.; Xiong, L.; Yuan, J.; Wang, J.; Jian, J.; et al. Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer. Int. J. Mol. Sci. 2023, 24, 2746. [Google Scholar] [CrossRef]
  58. van Pelt, G.W.; Kjær-Frifeldt, S.; van Krieken, J.H.J.M.; Al Dieri, R.; Morreau, H.; Tollenaar, R.A.E.M.; Sørensen, F.B.; Mesker, W.E. Scoring the tumor-stroma ratio in colon cancer: Procedure and recommendations. Virchows Arch. 2018, 473, 405–412. [Google Scholar] [CrossRef] [Green Version]
  59. Parmar, M.K.; Torri, V.; Stewart, L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat. Med. 1998, 17, 2815–2834. [Google Scholar] [CrossRef]
  60. Yusuf, S.; Peto, R.; Lewis, J.; Collins, R.; Sleight, P. Beta blockade during and after myocardial infarction: An overview of the randomized trials. Prog. Cardiovasc. Dis. 1985, 27, 335–371. [Google Scholar] [CrossRef] [PubMed]
  61. Hemmings, C. Is carcinoma a mesenchymal disease? The role of the stromal microenvironment in carcinogenesis. Pathology 2013, 45, 371–381. [Google Scholar] [CrossRef]
  62. Curry, J.M.; Sprandio, J.; Cognetti, D.; Luginbuhl, A.; Bar-ad, V.; Pribitkin, E.; Tuluc, M. Tumor microenvironment in head and neck squamous cell carcinoma. Semin. Oncol. 2014, 41, 217–234. [Google Scholar] [CrossRef] [Green Version]
  63. Chung, H.W.; Lim, J.B. Role of the tumor microenvironment in the pathogenesis of gastric carcinoma. World J. Gastroenterol. 2014, 20, 1667–1680. [Google Scholar] [CrossRef] [PubMed]
  64. Shekhar, M.P.; Pauley, R.; Heppner, G. Host microenvironment in breast cancer development: Extracellular matrix-stromal cell contribution to neoplastic phenotype of epithelial cells in the breast. Breast Cancer Res. 2003, 5, 130–135. [Google Scholar] [CrossRef] [PubMed]
  65. Pietras, K.; Ostman, A. Hallmarks of cancer: Interactions with the tumor stroma. Exp. Cell Res. 2010, 316, 1324–1331. [Google Scholar] [CrossRef] [PubMed]
  66. Zhang, R.; Song, W.; Wang, K.; Zou, S. Tumor-stroma ratio(TSR) as a potential novel predictor of prognosis in digestive system cancers: A meta-analysis. Clin. Chim. Acta Int. J. Clin. Chem. 2017, 472, 64–68. [Google Scholar] [CrossRef]
  67. Yoshizawa, A.; Motoi, N.; Riely, G.J.; Sima, C.S.; Gerald, W.L.; Kris, M.G.; Park, B.J.; Rusch, V.W.; Travis, W.D. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: Prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod. Pathol. Off. J. United States Can. Acad. Pathol. Inc. 2011, 24, 653–664. [Google Scholar] [CrossRef] [Green Version]
  68. Warth, A.; Muley, T.; Meister, M.; Stenzinger, A.; Thomas, M.; Schirmacher, P.; Schnabel, P.A.; Budczies, J.; Hoffmann, H.; Weichert, W. The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2012, 30, 1438–1446. [Google Scholar] [CrossRef]
Medicina 59 01258 g001 550
Figure 1. Flow chart of the searching strategy.
Figure 1. Flow chart of the searching strategy.
Medicina 59 01258 g001
Table
Table 1. Main characteristics of eligible studies.
Table 1. Main characteristics of eligible studies.
Author and
Publication Year		LocationOrganCriterion for High TSRNumber of
Patients		Tumor–Stroma Ratio
HighLow
Aboelnasr 2023 [39]EgyptColorectum50%1036736
Alessandrini 2022 [40]ItalyLarynx50%432914
Almangush 2018 [8]Finland/BrazilHead and neck50%31122289
Aurello 2017 [9]ItalyStomach50%1064165
Chen 2015 [10]ChinaOvary50%838575263
Courrech Staal 2010 [11]NetherlandsEsophagus50%936033
de Kruijf 2011 [12]NetherlandsBreast50%574186388
Dekker 2013 [13]NetherlandsBreast50%403241162
Dourado 2020 [14]FinlandHead and neck50%254142112
Geessink 2019 [15]NetherlandsColorectum50%1298742
Goyal 2021 [41]IndiaGallbladder50%965640
Hansen 2018 [16]DenmarkColorectum50%623329
He 2021 [42]ChinaEsophagus50%270113157
Huang 2022 [43]ChinaHead and neck50%1518467
Huijbers 2013 [17]UKColorectum50%710503207
Huijbers 2018 [18]UKColorectum50%965642323
Ichikawa 2018 [19]JapanLung50%1273592
Inoue 2022 [44]JapanColorectum85%200100100
Kairaluoma 2020 [20]FinlandLiver50%473413
Kang 2021 [45]KoreaColorectum50%26618581
Kang 2023 [46]ChinaHead and neck50%1135647
Karpathiou 2019 [2]FranceHead and neck30%266141125
50%26620660
Kemi 2018 [21]SwedenStomach50%583241342
Kim 2022 [47]KoreaStomach40%1577285
Labiche 2010 [22]FranceOvary50%1949896
Li 2017 [23]ChinaGallbladder50%513219
Li 2020 [48]ChinaPancreas
Developing cohort50%20712087
Validation cohort50%19311281
Liu 2014 [24]ChinaCervix50%18414737
Lv 2015 [25]ChinaLiver50%30022575
Mascitti 2020 [26]ItalyHead and neck50%211NDND
Öztürk 2022 [49]TürkiyeBreast50%105104101
Panayiotou 2015 [27]UKEndometriumND39934554
Peng 2018 [28]ChinaStomach50%494254240
Pongsuvareeyakul 2015 [29]ThailandCervix50%1319338
Qian 2021 [50]ChinaBreast55.5%24093147
Qiu 2022 [51]ChinaHead and neck50%581283298
Sandberg 2018 [30]NetherlandsColorectum50%715120
Scheer 2017 [31]NetherlandsColorectum30%15411836
70%15448106
Silva 2022 [52]BrazilHead and neck50%95NANA
Smit 2020 [53]NetherlandsLung50%1747995
Uzun 2022 [54]TürkiyeGallbladder50%281513
Vogelaar 2016 [32]NetherlandsColorectum50%975740
Xi 2017 [33]ChinaLung50%26122338
Xu 2020 [34]ChinaBreast 50%260146114
Xu 2023 [55]ChinaUrinary tract50%1015622393
Yan 2022 [56]ChinaBreast33.5%24015387
Zengin 2019 [35]TürkiyeColorectum50%885236
Zhang 2014 [36]ChinaHead and neck50%935142
Zhang 2015 [37]ChinaLung50%404302102
Zheng 2023 [57]ChinaUrinary bladder 45.7%1339439
Zong 2020 [38]ChinaCervix50%38431767
TSR, tumor–stroma ratio; UK, United Kingdom; ND, no data.
Table
Table 2. Results of quality assessment using the Newcastle–Ottawa Scale for eligible studies.
Table 2. Results of quality assessment using the Newcastle–Ottawa Scale for eligible studies.
Author and
Publication Year		Is the Case Definition AdequateRepresentativeness of the CasesSelection of ControlsDefinition of ControlsComparability of Cases and Controls on the Basis of the Design or AnalysisAscertainment of ExposureSame Method of Ascertainment for Cases and ControlsNon-Response RateQuality Score
Aboelnasr 2023 [39]11-121118
Alessandrini 2022 [40]11-121118
Almangush 2018 [8]11-121118
Aurello 2017 [9]11-121118
Chen 2015 [10]11-121118
Courrech Staal 2010 [11]11-121118
de Kruijf 2011 [12]11-121118
Dekker 2013 [13]11-121118
Dourado 2020 [14]11-121118
Geessink 2019 [15]11-121118
Goyal 2021 [41]11-121118
Hansen 2018 [16]11-121118
He 2021 [42]11-121118
Huang 2022 [43]11-121118
Huijbers 2013 [17]11-121118
Huijbers 2018 [18]11-121118
Ichikawa 2018 [19]11-121118
Inoue 2022 [44]11-121118
Kairaluoma 2020 [20]11-121118
Kang 2021 [45]11-121118
Kang 2023 [46]11-121118
Karpathiou 2019 [2]11-121118
Kemi 2018 [21]11-121118
Kim 2022 [47]11-121118
Labiche 2010 [22]11-121118
Li 2017 [23]11-121118
Li 2020 [48]11-121118
Liu 2014 [24]11-121118
Lv 2015 [25]11-121118
Mascitti 2020 [26]11-121118
Öztürk 2022 [49]11-121118
Panayiotou 2015 [27]11-121118
Peng 2018 [28]11-121118
Pongsuvareeyakul 2015 [29]11-121118
Qian 2021 [50]11-121118
Qiu 2022 [51]11-121118
Sandberg 2018 [30]11-121118
Scheer 2017 [31]11-121118
Silva 2022 [52]11-121118
Smit 2020 [53]11-121118
Uzun 2022 [54]11-121118
Vogelaar 2016 [32]11-121118
Xi 2017 [33]11-121118
Xu 2020 [34]11-121118
Xu 2023 [55]11-121118
Yan 2022 [56]11-121118
Zengin 2019 [35]11-121118
Zhang 2014 [36]11-121118
Zhang 2015 [37]11-121118
Zheng 2023 [57]11-121118
Zong 2020 [38]11-121118
Table
Table 3. The estimated rates of high tumor–stroma ratio in various malignant tumors.
Table 3. The estimated rates of high tumor–stroma ratio in various malignant tumors.
Number of
Subsets		Fixed Effect
(95% CI)		Heterogeneity Test
(p-Value)		Random Effect
(95% CI)		Egger’s Test
(p-Value)
Overall520.577 (0.588, 0.605)<0.0010.605 (0.565, 0.644)0.638
 Breast60.483 (0.460, 0.506)<0.0010.501 (0.391, 0.612)0.331
 Cervix30.794 (0.762, 0.823)0.0190.785 (0.713, 0.842)0.375
 Colorectum120.647 (0.630, 0.665)<0.0010.622 (0.556, 0.683)0.300
 Endometrium10.865 (0.827, 0.895)1.0000.865 (0.827, 0.895)-
 Esophagus20.475 (0.423, 0.527)<0.0010.529 (0.312, 0.736)-
 Gallbladder30.588 (0.514, 0.659)0.7220.588 (0.514, 0.659)0.820
 Head and neck80.578 (0.556, 0.600)<0.0010.594 (0.513, 0.671)0.393
 Larynx10.674 (0.523, 0.797)1.0000.674 (0.523, 0.797)-
 Liver20.746 (0.698, 0.789)0.6970.746 (0.698, 0.789)-
 Lung40.648 (0.613, 0.680)<0.0010.606 (0.343, 0.819)0.544
 Ovary20.650 (0.620, 0.679)<0.0010.601 (0.417, 0.761)-
 Pancreas20.580 (0.531, 0.627)0.9900.580 (0.531, 0.627)-
 Stomach40.454 (0.427, 0.481)0.0050.448 (0.387, 0.509)0.758
 Urinary tract20.623 (0.595, 0.651)0.0360.653 (0.556, 0.738)-
Criteria
 <50%50.603 (0.570, 0.634)<0.0010.624 (0.515, 0.721)0.285
 50%430.600 (0.591, 0.609)<0.0010.609 (0.567, 0.649)0.630
 >50%30.408 (0.368, 0.448)0.0010.399 (0.302, 0.506)0.605
CI, confidence interval.
Table
Table 4. The correlation between high tumor–stroma ratio and overall survival in various malignant tumors.
Table 4. The correlation between high tumor–stroma ratio and overall survival in various malignant tumors.
Number of
Subsets		Fixed Effect
(95% CI)		Heterogeneity Test
(p-Value)		Random Effect
(95% CI)		Egger’s Test
(p-Value)
Overall400.657 (0.616, 0.701)<0.0010.631 (0.542, 0.734)0.363
 Breast20.645 (0.487, 0.856)0.2840.630 (0.443, 0.896)-
 Cervix30.377 (0.258, 0.551)0.3840.377 (0.258, 0.551)0.785
 Colorectum100.643 (0.553, 0.747)<0.0010.588 (0.429, 0.804)0.308
 Endometrium12.510 (1.223, 5.152)1.0002.510 (1.223, 5.152)-
 Esophagus20.406 (0.294, 0.559)0.8540.406 (0.294, 0.559)-
 Gallbladder30.574 (0.346, 0.954)0.1320.568 (0.276, 1.169)0.152
 Head and neck40.610 (0.496, 0.750)0.1080.563 (0.400, 0.792)0.033
 Liver20.503 (0.316, 0.802)0.1400.538 (0.262, 1.105)-
 Lung40.719 (0.574, 0.900)0.0010.843 (0.482, 1.474)0.246
 Ovary20.834 (0.711, 0.978)0.5520.834 (0.711, 0.978)-
 Pancreas21.957 (1.443, 2.654)0.7791.957 (1.443, 2.654)-
 Stomach30.498 (0.421, 0.589)0.0630.456 (0.324, 0.641)0.214
 Urinary tract20.599 (0.491, 0.730)0.0480.636 (0.417, 0.971)-
Criteria
 >50%30.748 (0.605, 0.924)0.6030.748 (0.605, 0.924)0.388
 50%340.639 (0.596, 0.684)<0.0010.593 (0.501, 0.702)0.206
 >50%20.728 (0.483, 1.095)0.3780.728 (0.483, 1.095)-
CI, confidence interval.
Table
Table 5. The correlation between high tumor–stroma ratio and disease-free survival in various malignant tumors.
Table 5. The correlation between high tumor–stroma ratio and disease-free survival in various malignant tumors.
Number of
Subsets		Fixed Effect
(95% CI)		Heterogeneity Test
(p-Value)		Random Effect
(95% CI)		Egger’s Test
(p-Value)
Overall290.571 (0.522, 0.623)<0.0010.564 (0.476, 0.669)0.551
 Breast40.517 (0.423, 0.632)0.3130.517 (0.415, 0.645)0.848
 Cervix30.447 (0.307, 0.650)0.5190.447 (0.307, 0.650)0.351
 Colorectum60.609 (0.518, 0.716)0.2000.609 (0.490, 0.759)0.826
 Endometrium12.180 (1.146, 4.146)1.0002.180 (1.146, 4.146)-
 Esophagus10.458 (0.281, 0.746)1.0000.458 (0.281, 0.746)-
 Head and neck80.592 (0.495, 0.710)<0.0010.661 (0.430, 1.017)0.366
 Larynx10.089 (0.025, 0.323)1.0000.089 (0.025, 0.323)-
 Lung30.628 (0.497, 0.794)0.5190.628 (0.497, 0.794)0.725
 Stomach20.181 (0.090, 0.364)0.2390.176 (0.077, 0.405)-
Criteria
 <50%30.475 (0.348, 0.648)0.0090.466 (0.222, 0.981)0.959
 50%230.560 (0.509, 0.617)<0.0010.545 (0.457, 0.650)0.320
 >50%20.618 (0.430, 0.889)0.8010.618 (0.430, 0.889)-
CI, confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pyo, J.-S.; Kim, N.Y.; Min, K.-W.; Kang, D.-W. Significance of Tumor–Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors. Medicina 2023, 59, 1258. https://doi.org/10.3390/medicina59071258

AMA Style

Pyo J-S, Kim NY, Min K-W, Kang D-W. Significance of Tumor–Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors. Medicina. 2023; 59(7):1258. https://doi.org/10.3390/medicina59071258

Chicago/Turabian Style

Pyo, Jung-Soo, Nae Yu Kim, Kyueng-Whan Min, and Dong-Wook Kang. 2023. "Significance of Tumor–Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors" Medicina 59, no. 7: 1258. https://doi.org/10.3390/medicina59071258

Article Metrics

Back to TopTop