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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 9153

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Special Issue Editors

Dr. Li Liu

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Guest Editor

Associate Professor, Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
Interests: bioluminescence, fluorescence and photoacoustic imaging application in cancer research

Prof. Dr. Yicheng Ni

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Guest Editor

Theranostic Lab, Department of Imaging and Pathology, Biomedical Group, KU Leuven, Herestraat 49, Leuven, Belgium
Interests: imaging-navigated translational theragnostic research
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Special Issue Information

Dear Colleagues,

Medical imaging is playing an ever increasing role in clinical, experimental, and translational oncology for population surveillance, patient screening, diagnosis assurance, cancer staging, therapeutic evaluation, prognosis assessment and development of novel anticancer approaches.

Recent advances in oncologic imaging include: 1) multimodality (US, CT, MRI, PET, SPECT, optical imaging) and multiparametric (morphological, functional, metabolic) applications; 2) precision medicine driven by molecular imaging and nano-technologies 3) theranostics that utilize molecular imaging to identify cancer patient-specific biomarkers to guide individualized treatment decisions; and 4) radiomics assisted by artificial intelligence, big data analytics and deep learning for multi-feature characterization. This Special Issue will update and highlight the state of the art with respect to cancer imaging.

Dr. Li Liu
Prof. Dr. Yicheng Ni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

medical imaging
cancer
oncology
molecular imaging
theragnostics

Published Papers (6 papers)

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Research

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10 pages, 6249 KiB

Open AccessArticle

AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology

by Andreas Weber, Kathrin Enderle-Ammour, Konrad Kurowski, Marc C. Metzger, Philipp Poxleitner, Martin Werner, René Rothweiler, Jürgen Beck, Jakob Straehle, Rainer Schmelzeisen, David Steybe and Peter Bronsert

Cancers 2024, 16(4), 689; https://doi.org/10.3390/cancers16040689 - 6 Feb 2024

Viewed by 1102

Abstract

Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k₁ = 2845 cm⁻¹ and k₂ = 2930 cm⁻¹. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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22 pages, 5156 KiB

Open AccessArticle

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

by Chao Li, Haiyan Chen, Bicheng Zhang, Yimin Fang, Wenzheng Sun, Dang Wu, Zhuo Su, Li Shen and Qichun Wei

Cancers 2023, 15(21), 5134; https://doi.org/10.3390/cancers15215134 - 25 Oct 2023

Cited by 3 | Viewed by 993

Abstract

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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14 pages, 2157 KiB

Open AccessArticle

Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy

by Chad A. Arledge, William N. Crowe, Lulu Wang, John Daniel Bourland, Umit Topaloglu, Amyn A. Habib and Dawen Zhao

Cancers 2023, 15(10), 2703; https://doi.org/10.3390/cancers15102703 - 10 May 2023

Cited by 1 | Viewed by 1578

Abstract

The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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13 pages, 1797 KiB

Open AccessArticle

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

by Huei-Yi Tsai, Tsung-Yu Tsai, Chia-Hui Wu, Wei-Shiuan Chung, Jo-Ching Wang, Jui-Sheng Hsu, Ming-Feng Hou and Ming-Chung Chou

Cancers 2022, 14(24), 6261; https://doi.org/10.3390/cancers14246261 - 19 Dec 2022

Cited by 5 | Viewed by 1714

Abstract

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7–0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical–radiomics models were trained for each algorithm. Radiomics and clinical–radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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13 pages, 2730 KiB

Open AccessArticle

Impact of Blood–Brain Barrier to Delivering a Vascular-Disrupting Agent: Predictive Role of Multiparametric MRI in Rodent Craniofacial Metastasis Models

by Shuncong Wang, Yuanbo Feng, Lei Chen, Jie Yu, Yue Li and Yicheng Ni

Cancers 2022, 14(23), 5826; https://doi.org/10.3390/cancers14235826 - 26 Nov 2022

Viewed by 1288

Abstract

Vascular-disrupting agents (VDAs) have shown a preliminary anti-cancer effect in extracranial tumors; however, the therapeutic potential of VDAs in intracranial metastatic lesions remains unclear. Simultaneous intracranial and extracranial tumors were induced by the implantation of rhabdomyosarcoma in 15 WAG/Rij rats. Pre-treatment characterizations were performed at a 3.0 T clinical magnet including a T2 relaxation map, T1 relaxation map, diffusion-weighted imaging (DWI), and perfusion-weighted imaging (PWI). Shortly afterward, a VDA was intravenously given and MRI scans at 1 h, 8 h, and 24 h after treatment were performed. In vivo findings were further confirmed by postmortem angiography and histopathology staining with H&E, Ki67, and CD31. Before VDA treatment, better perfusion (AUC30: 0.067 vs. 0.058, p < 0.05) and AUC300 value (0.193 vs. 0.063, p < 0.001) were observed in extracranial lesions, compared with intracranial lesions. After VDA treatment, more significant and persistent perfusion deficiency measured by PWI (AUC30: 0.067 vs. 0.008, p < 0.0001) and a T1 map (T1 ratio: 0.429 vs. 0.587, p < 0.05) were observed in extracranial tumors, in contrast to the intracranial tumor (AUC30: 0.058 vs. 0.049, p > 0.05, T1 ratio: 0.497 vs. 0.625, p < 0.05). Additionally, significant changes in the T2 value and apparent diffusion coefficient (ADC) value were observed in extracranial lesions, instead of intracranial lesions. Postmortem angiography and pathology showed a significantly larger H&E-stained area of necrosis (86.2% vs. 18.3%, p < 0.0001), lower CD31 level (42.7% vs. 54.3%, p < 0.05), and lower Ki67 level (12.2% vs. 32.3%, p < 0.01) in extracranial tumors, compared with intracranial lesions. The BBB functioned as a barrier against the delivery of VDA into intracranial tumors and multiparametric MRI may predict the efficacy of VDAs on craniofacial tumors. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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Review

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17 pages, 2229 KiB

Open AccessReview

Intraoperative Imaging in Hepatopancreatobiliary Surgery

by Tereza Husarova, William M. MacCuaig, Isabel S. Dennahy, Emma J. Sanderson, Barish H. Edil, Ajay Jain, Morgan M. Bonds, Molly W. McNally, Katerina Menclova, Jiri Pudil, Pavel Zaruba, Radek Pohnan, Christina E. Henson, William E. Grizzle and Lacey R. McNally

Cancers 2023, 15(14), 3694; https://doi.org/10.3390/cancers15143694 - 20 Jul 2023

Cited by 1 | Viewed by 1472

Abstract

Hepatopancreatobiliary surgery belongs to one of the most complex fields of general surgery. An intricate and vital anatomy is accompanied by difficult distinctions of tumors from fibrosis and inflammation; the identification of precise tumor margins; or small, even disappearing, lesions on currently available imaging. The routine implementation of ultrasound use shifted the possibilities in the operating room, yet more precision is necessary to achieve negative resection margins. Modalities utilizing fluorescent-compatible dyes have proven their role in hepatopancreatobiliary surgery, although this is not yet a routine practice, as there are many limitations. Modalities, such as photoacoustic imaging or 3D holograms, are emerging but are mostly limited to preclinical settings. There is a need to identify and develop an ideal contrast agent capable of differentiating between malignant and benign tissue and to report on the prognostic benefits of implemented intraoperative imaging in order to navigate clinical translation. This review focuses on existing and developing imaging modalities for intraoperative use, tailored to the needs of hepatopancreatobiliary cancers. We will also cover the application of these imaging techniques to theranostics to achieve combined diagnostic and therapeutic potential. Full article

(This article belongs to the Special Issue Recent Advances in Oncology Imaging)

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