Search Results (530)

Skin metastases represent a rare finding in dermatological practice, but their presence signifies an advanced disease and usually portends a poor prognosis. They commonly arise as multiple painless nodules in patients with a cancer history. Differential diagnoses are challenging, and zosteriform metastases should not be mistaken for herpes zoster. Dermoscopy typically reveals a white, structureless pattern. A skin biopsy with routine hematoxylin–eosin staining is essential for an accurate diagnosis, while immunohistochemistry is particularly useful in cases of anaplastic tumors. Breast cancer is the most common cause of skin metastasis in women, and lung cancer is the most common in men. The life expectancy after diagnosis is generally low. Cutaneous metastasectomy, electrochemotherapy, and radiotherapy are generally regarded as beneficial for palliative purposes. Intralesional cryosurgery was found to be beneficial in a few case series. Systemic immunotherapy can induce the regression of cutaneous metastases in selected patients. Full article

Background: The diagnostic work-up of small peripheral pulmonary nodules (PPNs) is becoming increasingly important, especially in light of the upcoming lung cancer screening programs and recommendations in practice. The systematic clinical introduction of the ION robotic-assisted navigational bronchoscopy (RNB) system represents a significant innovation in Germany, whereas clinical experience in the United States has already yielded promising results. The objective of this study is to present the outcomes of the first 50 patients examined with the ION system at our institutions. Materials and Methods: This is a retrospective, single-center analysis. We included the first 50 consecutive patients who underwent diagnostic evaluation of pulmonary nodules using the ION-RNB system, either in the Department of Thoracic Surgery or the Department of Pulmonology. Results: A total of 50 patients were evaluated, including 24 from the Department of Thoracic Surgery and 26 from the Department of Pulmonology. The pulmonary nodules were found in the peripheral third of the lung in 74% of cases, in the middle third in 18% of cases, and in the central third in 8% of cases. The mean lesion size was 1.64 cm (±0.91 cm). In all, 84% of the nodules were solid, 4% were subsolid, and 12% presented as ground-glass opacities (GGOs). Cone beam computed tomography (CBCT) was used to confirm tool-in-lesion position in 68% of cases compared to C-arm fluoroscopy in 32%. Additionally, radial endobronchial ultrasound (rEBUS) was applied in 30% of procedures. The overall diagnostic yield, independent of imaging modality or histological processing method, was 78%. When CBCT and formalin-fixed paraffin-embedded (FFPE) histological analysis were utilized, the diagnostic yield exceeded 90%. Conclusions: Initial clinical experience with the ION-RNB system in Germany shows encouraging results. The high diagnostic accuracy underlines the system’s potential for evaluating peripheral pulmonary lesions precisely. The use of advanced imaging techniques, particularly CBCT, and the choice of histopathological processing methods are critical variables in optimizing patient-centered diagnostic pathways. Further prospective studies are warranted to assess the long-term clinical utility of robotic-assisted bronchoscopy in diverse clinical settings. Full article

Objectives: Overall survival (OS) of patients with stage 0 adenocarcinoma in situ (AIS) of the lung is not well characterized in the U.S. Specifically, there are a lack of data regarding the OS of patients with stage 0 AIS who do not receive treatment. We compared OS among stage 0 AIS patients who received surgery and those who received no treatment. Methods: OS of patients with stage 0 (TIS, N0, M0) AIS of the lung who received surgery versus no treatment from 2010 to 2018 in the National Cancer Data Base was evaluated using multivariable Cox proportional hazards modeling and propensity score-matched analysis. Predictors of surgery were evaluated using multivariable logistic regression. Survival outcomes based on surgical approach were evaluated in a propensity score-matched subgroup analysis. Results: Of the 897 patients who were diagnosed with stage 0 AIS, 716 (79.8%) underwent surgical resection. A propensity score-matched analysis of 134 patients who received no treatment and 134 patients who underwent surgery showed that the surgical group had a significantly improved OS at five-years 85.8% (95% CI: 74.2–92.4%) compared to the group who received no treatment 62.8% (95% CI: 50.1–72.7%) (log-rank, p < 0.0001). Subgroup propensity score-matched analysis showed no significant differences in OS at five-years in the surgical group consisting of 201 patients who underwent a wedge resection 90.8% (95% CI: 83.8–94.8) compared to 201 patients who underwent a lobectomy 94.9% (95% CI: 89.9–97.4%) (log-rank, p = 0.19). Conclusions: In this national analysis, stage 0 AIS patients who underwent surgery had significantly better OS when compared to patients who did not receive treatment. Full article

Early diagnosis of lung cancer, essential for reducing its high mortality rate, is currently challenging, partly due to the lack of specific biomarkers. Here, we attempted to develop a noninvasive and potentially sensitive screening method based on the proteomic analysis of unstimulated and stimulated saliva samples, collected by passive drooling and salivary swabs, respectively, from healthy heavy smokers enrolled in a nonprofit screening project. Protein content analyzed before and after sample cryopreservation for various periods and the associated two-dimensional electrophoresis revealed that protein extraction after short-term cryopreservation prevented the loss of detectable proteins. Mass spectrometric analysis of these electrophoretically resolved proteins revealed the presence of salivary proteins whose levels may be dysregulated in various types of lung cancer. Finally, in pilot experiments conducted on stimulated saliva from a patient with a lung cancer nodule, we detected altered content or selective presence of proteins involved in lung carcinogenesis, such as serpin B3 or the proteins S100A14 and aldoketoreductase-A1, respectively. While acknowledging that these findings require further validation, we believe that the use of saliva and related proteomic analyses may contribute to the identification of potential early lung cancer biomarkers, which could hopefully improve clinical management of the tumor and patient survival. Full article

Background: Advances in screening programs have led to increased detection of early-stage non-small cell lung cancer (NSCLC), including synchronous or metachronous nodules amenable to surgical resection. Patients requiring contralateral anatomical lung resections present a unique surgical challenge due to potential impairments in lung function and the complexities of one-lung ventilation. This study evaluates the feasibility, safety, and perioperative outcomes of robotic-assisted thoracic surgery (RATS) for contralateral anatomical lung resections in patients with NSCLC. Methods: A retrospective analysis was conducted on 20 patients who underwent RATS contralateral anatomical resection between January 2019 and June 2024. Preoperative pulmonary function, perioperative characteristics, and oncological outcomes were assessed. Operative parameters, including conversion rates, intraoperative oxygenation, need for extracorporeal membrane oxygenation (ECMO), and postoperative complications, were recorded. Results: Seventy percent of the patients underwent surgery for metachronous tumors. The median forced expiratory volume in 1 s (FEV1) was 75.94% (66.62–89.24). The most common resection was segmentectomy (65.0%). The median operative time was 148.0 min (108.0–194.75). There were no conversions to open surgery or ECMO requirements. Intraoperative parameters remained stable (median FiO₂: 0.8; lowest SaO₂: 92.0%). Complications occurred in 25% of the patients, mostly Clavien–Dindo grade 2. No in-hospital, 30-day, or 90-day mortality was observed. Conclusions: Robotic-assisted contralateral anatomical lung resection is a feasible and safe approach for patients with previous contralateral surgery, supporting its role as a minimally invasive alternative for complex surgical cases. Full article

Background/Objectives: To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. Methods: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. Results: RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (p < 0.001 for LLN/SLN and p = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. Conclusions: Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Lung nodule detection and segmentation are essential tasks in computer-aided diagnosis (CAD) systems for early lung cancer screening. With the growing availability of CT data and deep learning models, researchers have explored various strategies to improve the performance of these tasks. This review focuses on Multi-Task Learning (MTL) approaches, which unify or cooperatively integrate detection and segmentation by leveraging shared representations. We first provide an overview of traditional and deep learning methods for each task individually, then examine how MTL has been adapted for medical image analysis, with a particular focus on lung CT studies. Key aspects such as network architectures and evaluation metrics are also discussed. The review highlights recent trends, identifies current challenges, and outlines promising directions toward more accurate, efficient, and clinically applicable CAD solutions. The review demonstrates that MTL frameworks significantly enhance efficiency and accuracy in lung nodule analysis by leveraging shared representations, while also identifying critical challenges such as task imbalance and computational demands that warrant further research for clinical adoption. Full article

Dark sweet cherries (DSC) phytochemicals have emerged as a promising dietary strategy to combat triple-negative breast cancer (TNBC). This study explored the effects of DSC extract rich in anthocyanins (ACN) as a chemopreventive agent and as a complement to doxorubicin (DOX) in treating TNBC tumors and metastasis using a 4T1 syngeneic animal model. Initiating ACN intake as a chemopreventive one week before 4T1 cell implantation significantly delayed tumor growth without any signs of toxicity. Both DOX treatment and the combination of DOX-ACN effectively delayed tumor growth rate, but DOX-ACN allowed for body weight gain, which was hindered by DOX alone. As a chemopreventive, ACN downregulated metastasis- and immune-suppression-related genes, including STAT3, Snail1, mTOR, SIRT1, TGFβ1, IKKβ, and those unaffected by DOX alone, such as HIF, Cd44, and Rgcc32. Correlations between mRNA levels seen in control and DOX groups were absent in ACN and/or DOX-ACN groups, indicating that Cd44, mTOR, Rgcc32, SIRT1, Snail1, and TGFβ1 may be ACN targets. The DOX-ACN treatment showed a trend toward enhanced efficacy involving CREB, PI3K, Akt-1, and Vim compared to DOX alone. Particularly, ACN significantly suppressed lung metastasis compared to the other groups. ACN also decreased the frequency and incidence of metastasis in the liver, heart, kidneys, and spleen, while their metastatic area (%) and number of breast cancer (BC) metastatic tumor nodules were lowered without reaching significance. Further research is needed to explore the efficacy of combining ACN with drug therapy in the context of drug resistance. Full article

Introduction: Radiomics is the extraction of non-invasive and reproducible quantitative imaging features, which may yield mineable information for clinical practice implementation. Quantification of lung function through radiomics could play a role in the management of patients with pulmonary lesions. The aim of this study is to test the capability of radiomic features to predict pulmonary function parameters, focusing on the diffusing capacity of lungs to carbon monoxide (DL_CO). Methods: Retrospective data were retrieved from electronical medical records of patients treated with Stereotactic Body Radiation Therapy (SBRT) at a single institution. Inclusion criteria were as follows: (1) SBRT treatment performed for primary early-stage non-small cell lung cancer (ES-NSCLC) or oligometastatic lung nodules, (2) availability of simulation four-dimensional computed tomography (4DCT) scan, (3) baseline spirometry data availability, (4) availability of baseline clinical data, and (5) written informed consent for the anonymized use of data. The gross tumor volume (GTV) was segmented on 4DCT reconstructed phases representing the moment of maximum inhalation and maximum exhalation (Phase 0 and Phase 50, respectively), and radiomic features were extracted from the lung parenchyma subtracting the lesion/s. An iterative algorithm was clustered based on correlation, while keeping only those most associated with baseline and post-treatment DL_CO. Three models were built to predict DL_CO abnormality: the clinical model—containing clinical information; the radiomic model—containing the radiomic score; the clinical-radiomic model—containing clinical information and the radiomic score. For the models just described, the following were constructed: Model 1 based on the features in Phase 0; Model 2 based on the features in Phase 50; Model 3 based on the difference between the two phases. The AUC was used to compare their performances. Results: A total of 98 patients met the inclusion criteria. The Charlson Comorbidity Index (CCI) scored as the clinical variable most associated with baseline DL_CO (p = 0.014), while the most associated features were mainly texture features and similar among the two phases. Clinical-radiomic models were the best at predicting both baseline and post-treatment abnormal DL_CO. In particular, the performances for the three clinical-radiomic models at predicting baseline abnormal DL_CO were AUC₁ = 0.72, AUC₂ = 0.72, and AUC₃ = 0.75, for Model 1, Model 2, and Model 3, respectively. Regarding the prediction of post-treatment abnormal DL_CO, the performances of the three clinical-radiomic models were AUC₁ = 0.91, AUC₂ = 0.91, and AUC₃ = 0.95, for Model 1, Model 2, and Model 3, respectively. Conclusions: This study demonstrates that radiomic features extracted from healthy lung parenchyma on a 4DCT scan are associated with baseline pulmonary function parameters, showing that radiomics can add a layer of information in surrogate models for lung function assessment. Preliminary results suggest the potential applicability of these models for predicting post-SBRT lung function, warranting validation in larger, prospective cohorts. Full article

Protein functional effector (pfe)RNAs were introduced in 2015 as PIWI-interacting-like small noncoding (nc)RNAs and were later categorized as a novel group based on being 2′-O-methylated at their 3′-end, directly binding and affecting protein function, but not levels, and not matching known RNAs. Here, we document that human pfeRNAs match fragments of GenBank database-annotated human ncRNAs. PDLpfeRNAa matches the 3′-half fragment of a mitochondrial transfer (t)RNA, and PDLpfeRNAb matches a 28S ribosomal (r)RNA fragment. These PDLpfeRNAs are known to bind to tumor programmed death ligand (PD-L)1, enhancing or inhibiting its interaction with lymphocyte PD-1 and consequently tumor immune escape, respectively. In a validated 8-pfeRNA-set classifier for pulmonary nodule presence and benign vs. malignant nature, seven here match one or more of the following: transfer, micro, Y, PIWI, long (lnc)RNAs, and a PDLpfeRNAa fragment. The previously identified chromosomal locations of these pfeRNAs and their matches partially overlap. Another 2-pfeRNA set was previously determined to distinguish between controls, patients with pulmonary tuberculosis, and those with lung cancer. One pfeRNA, previously shown to bind p60-DMAD and affect apoptosis, complements small nucleolar RNA SNORD45C, matching smaller 18S rRNA and lncRNA segments. Thus, pfeRNAs appear to have a common origin with known multifunctional ncRNA fragments. Differential modification may contribute to the multifunctionality of ncRNAs. For instance, for tRNA fragments, stabilizing 3′-end 2′-O-methylation, 3′-aminoacylation, and glycosylation modifications may regulate protein function, translation, and extracellular effects, respectively. One ncRNA gene can encode multiple fragments, multiple genes can encode the same fragment, and differentially modified ncRNA fragments might synergize or antagonize each other. Full article

Background/Objectives: Lung cancer screening with low-dose computed tomography (LDCT) has reduced lung cancer mortality in high-risk smokers. However, the evidence on LDCT screening in the elderly is limited, with there being few older participants in major trials and ongoing debate about the benefits, risks, and appropriate age limits of LDCT. This study aimed to investigate the prevalence of pulmonary nodules and lung cancer detection rates in men aged 70 and above who underwent a single round of LDCT screening. Methods: We retrospectively analyzed data from elderly male participants aged 70 years or older who underwent a single low-dose CT lung cancer screening at the Veterans Health Service Medical Center between 2010 and 2023. The participants included those who requested screening or were asymptomatic but recommended by physicians. Individuals with prior lung cancer, symptoms suggestive of lung cancer, or suspicious findings on previous imaging were excluded. The nodule prevalence, lung cancer diagnoses, pathological subtypes, and clinical stages were reviewed. Results: A total of 1409 individuals with a mean age of 74.2 years were included. The median follow-up duration was 3.6 years. Among the included individuals, 1304 (92.6%) had a history of smoking. Positive nodules were detected in 179 patients (12.7%, 95% CI: 11.0–14.5%), and lung cancer was diagnosed in 31 patients (2.2%, 95% CI: 1.5–3.1%). Of the diagnosed cases, 14 (45.2%) were adenocarcinomas and 12 (38.7%) were squamous cell carcinomas. Nineteen patients (51.3%) were diagnosed with stage I or II cancer, while seven (22.6%) were diagnosed at stage IV. Conclusions: A single round of LDCT screening in elderly men resulted in a relatively high lung cancer detection rate, with over half of the diagnosed cases being identified at an early stage. This highlights the potential clinical benefit of even one-time screening in enabling timely treatment, which may still be feasible in older adults. However, potential harms such as overdiagnosis should also be considered. Full article

Background and Objectives: Immune checkpoint inhibitors (ICIs) have emerged as groundbreaking agents in cancer therapy; however, their immune-related adverse effects, especially pulmonary toxicity, significantly limit their use. This study aimed to determine the incidence and risk factors associated with ICI-induced pulmonary toxicity. Materials and Methods: We conducted a prospective observational study involving 126 patients aged ≥18 years with malignancies treated with ICIs between April 2022 and April 2024. Patients were followed every six months over a two-year period. Clinical, laboratory, and radiological data were collected to assess pulmonary toxicity. Results: The mean age of our patients was 62.93 ± 12.94 years, and 81% were male. The ICI-related pulmonary toxicity rate was 16.7%, and the all-cause mortality rate was 68.3%. In the analysis, the conditions associated with pulmonary toxicity were the type of malignancy, the presence of lung cancer, COPD, long-term ICI use, dyspnea, cough and sputum, the pre-ICI lung nodule mass, and high blood monocyte levels. Our regression analysis results for the determination of risk factors showed a 7.70-fold increase in the presence of cough symptoms, a 4.57-fold increase in the presence of COPD, a 0.998-fold increase for every 1 unit decrease in lymphocyte count, and an 11.75-fold increase in risk for a monocyte count of 130 or less. Conclusions: Our study’s findings suggest that patients with identifiable risk factors for pulmonary toxicity should undergo closer monitoring and early diagnostic evaluation during ICI therapy to reduce morbidity and mortality. Full article

Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integrating clinical metadata. Hamiltonian Monte-Carlo sampling (10 000 posterior draws) captured parameter uncertainty; performance was assessed with stratified five-fold cross-validation and on three independent multi-centre cohorts. On the locked internal test set, the model achieved 99.0% accuracy, AUC = 0.990 and macro-F1 = 0.987. External validation across 824 scans yielded a mean AUC of 0.933 and an expected calibration error $<0.034$, while eliminating false positives for benign nodules and providing voxel-level uncertainty maps. Uncertainty-aware Bayesian deep learning delivers state-of-the-art, well-calibrated lung-cancer risk predictions from a single CT scan, supporting personalised screening intervals and safe deployment in clinical workflows. Full article

The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often utilize channel attention mechanisms and intersection over union (IoU)-based loss functions. Yet, they frequently overlook spatial context and struggle to capture subtle variations in aspect ratios, which hinders their ability to detect small objects. In this study, we introduce an improved YOLOV11 framework that addresses these limitations through two primary components: a spatial squeeze-and-excitation (SSE) module that concurrently models channel-wise and spatial attention to enhance the discriminative features pertinent to nodules and explicit aspect ratio penalty IoU (EAPIoU) loss that imposes a direct penalty on the squared differences in aspect ratios to refine the bounding box regression process. Comprehensive experiments conducted on the LUNA16, LungCT, and Node21 datasets reveal that our approach achieves superior precision, recall, and mean average precision (mAP) across various IoU thresholds, surpassing previous state-of-the-art methods while maintaining computational efficiency. Specifically, the proposed SSE module achieves a precision of 0.781 on LUNA16, while the EAPIoU loss boosts mAP@50 to 92.4% on LungCT, outperforming mainstream attention mechanisms and IoU-based loss functions. These findings underscore the effectiveness of integrating spatially aware attention mechanisms with aspect ratio-sensitive loss functions for robust nodule detection. Full article