AI and Cancer Research ── 2026-10-25
New papers on cancer and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 13 themes, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
Read by cancer type: Cancer biology（4） · Breast（4） · Lung（4） · Pancreatic（0） · Prostate（0） · ACC（0） · Brain（1） · Ovarian（0） · Endometrial（1） · Gastric（1） · Liver（4） · Kidney（0） · Bladder（1）
Cancer molecular biology and AI
- Abstract
Diagnostic imaging represents one of the most promising clinical applications of artificial intelligence (AI) algorithms. The purpose of this study was to evaluate the customised convolutional neural network (CNN) SepNetDense for distinguishing between normal and abnormal canine brain magnetic resonance images (MRIs), with the aim of enhancing diagnostic efficiency and assisting radiologists in identifying abnormal images. The dataset comprised T1-weighted (T1w) pre- and post-contrast sequences in transverse, sagittal, and dorsal planes from 550 dogs, collected from four universities. Dogs were included if they had a complete clinical diagnosis confirmed either through histopathology or in accordance with current clinical consensus. Patients were randomly divided into a training set (n = 444), a validation set (n = 53), and a test set (n = 53). Each MRI was labelled on a slice-by-slice basis as normal or abnormal. The model was trained on 205 normal imaging datasets (e.g., extracranial aetiologies, idiopathic epilepsy, paroxysmal dyskinesia) and 239 abnormal ones (e.g., neoplasms, inflammatory lesions, other pathologies). The model correctly predicted 74% of the true normal slices in the test set as normal and 73% of the true abnormal as abnormal. A ROC analysis of the model’s prediction at the patient level revealed that, at a threshold of 51% abnormal slices per patient, the model reached an optimal balance of 83% sensitivity (95% CI 67–91%) and 78% (95% CI 54–91%) specificity, with a balanced accuracy of 80% (95% CI 69–92%). ANCOVA revealed that the CNN’s classification performance was influenced by multiple biological, technical, and institutional factors. Lesion diagnosis, institutional setting, and breed size had significant large effects, whereas body weight showed a significant medium effect. Significant interactions with large effect sizes were also observed between diagnosis and institute, as well as between breed size and weight. Additionally, a distinct pattern was observed in the distribution of prediction categories along the anatomical axes, with a trend towards better CNN performance in the central quartiles across all MRI sequences. Overall, the results demonstrate the feasibility of CNN-assisted triage of canine brain MRI studies and highlight diagnosis, institutional setting, breed size, and body weight as key determinants of model performance, emphasising the importance of heterogeneous datasets for the development of robust veterinary AI applications.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Abani S, Laue M, Dickinson PJ, De Decker S, Gutierrez-Quintana R, Ghiasvand S, et al. CNN model-based image classification for canine brain MRI abnormalities. Sci Rep. 2026 Oct 23;16(1):33913. doi:10.1038/s41598-026-71470-w.Checked: Abstract only - Abstract
Two of the most severe forms of neurological issues, which can be treated only when diagnosed with a high degree of accuracy with the help of MRI, are brain tumors and Alzheimer disease (AD).The project under analysis considers the datasets of MRI that are not balanced and refers to the Synthetic Minority Oversampling Technique (SMOTE) to improve the classification process.They included several ML and DL models including the RF, SVM, Voting Classifier, CNN, VGG16, ResNet50, and Inception V3, as well as the AlexNet with LSTM, Xception, and the new Transformer and LSTM Enhanced Attention-Based Explainable CNN (TLEABLCNN).TLEABLCNN model consistently performed better than its baseline models with a classification accuracy of 96.1% in Scenario A, 97.4% in Scenario B and 98.2% in Scenario C. It also possessed superior macro-precision, recall and F1-scores.To detect abnormalities in the brain; the object recognition models of YOLOv5, YOLOv8, YOLOv9 and YOLOv11 were employed.YOLOv9 got the highest best mean average precision (mAP) of 91.5%.YOLOv8 achieved the best accuracy of 91.8%.Moreover, XAI techniques, such as heatmaps, were employed to generate the heatmaps that revealed the most significant portions of MRI scans that influenced model predictions.This simplified the results and enhanced professional trust.These findings indicate the strength of the framework in the discovery of brain disorders which can be explained.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: [No authors listed]. TLEABLCNN: Brain and Alzheimer’s Disease Detection Using Attention-Based Explainable Deep Learning and SMOTE Using Imbalanced Brain MRI. IJDDT. 2026 Oct 22;16(76s). doi:10.25258/ijddt.16.76s.161.Checked: Abstract only
See all 4 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
Tumor-infiltrating lymphocytes (TILs) are biomarkers of the breast cancer immune microenvironment associated with treatment response and prognosis, but their assessment relies mainly on surgical histopathology. We aimed to develop and validate a preoperative model combining multiphase tumoral–peritumoral DCE-MRI radiomics with conventional features to predict TIL levels. This retrospective two-center study included 495 patients who underwent preoperative breast DCE-MRI followed by surgical resection as initial treatment. TILs were assessed on surgical specimens using a prespecified 10% cutoff commonly adopted in previous breast MRI-based TIL studies. Radiomic features from tumoral and 3-mm peritumoral regions across six DCE-MRI phases were combined with clinical, semantic MRI, and biopsy-derived features. Model performance was assessed by discrimination, calibration, incremental value, and clinical utility, and Shapley additive explanations (SHAP) analysis was used to assess feature contributions. The combined model achieved the AUC of 0.882 (95% confidence interval [CI]: 0.840–0.920), 0.777 (95% CI: 0.672–0.866), and 0.738 (95% CI: 0.637–0.826) in the training, internal validation, and external validation sets, respectively. In external validation, the combined model showed significant integrated discrimination improvement (IDI) over the conventional model (IDI = 0.105, P = 0.005) and the tumoral–peritumoral model (IDI = 0.098, P = 0.001). Decision curve analysis showed greater net benefit across most evaluated threshold probabilities. SHAP analysis identified later-phase peritumoral radiomic features as the dominant predictive factors for TIL estimation. Integrating multiphase tumoral–peritumoral DCE-MRI radiomics with conventional preoperative features showed potential for predicting TIL levels, with late-phase peritumoral features dominating the predictive performance. This model provides a promising preoperative tool for immune-related risk stratification and individualized multidisciplinary treatment planning.
Journal IF-equivalent: 3.8 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Yang W, Chen M, Cui Y, Xu W, Tian W, Jiang Y, et al. Preoperative prediction of breast cancer tumor-infiltrating lymphocytes levels with multiphase tumoral–peritumoral DCE-MRI radiomics and conventional features. BMC Med Imaging. 2026 Oct 23 [Epub ahead of print]. doi:10.1186/s12880-026-02930-y.Checked: Abstract only - Abstract
Background: Accurate identification of residual axillary lymph node (ALN) disease after neoadjuvant chemotherapy (NAC) is essential for treatment planning and for evaluating potential surgical de-escalation. We assessed the diagnostic performance of conventional ultrasonography (US), shear-wave elastography (SWE), artificial intelligence (AI)/radiomics, and multimodal ultrasound-based models for post-NAC axillary restaging.
Methods: PubMed, Embase, the Cochrane Library, and Web of Science were searched from inception through December 2025. Histopathology after axillary surgery was the reference standard, and residual axillary nodal disease (ypN+) was uniformly defined as the positive target condition. Study-level 2×2 tables were harmonized to this direction. Risk of bias and applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Bivariate random-effects models and hierarchical summary receiver operating characteristic (HSROC) curves were used for modality groups with a sufficient number of independent studies; the positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were pooled with random-effects models. Potentially overlapping cohorts were examined in sensitivity analyses. The study protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews; registration No. CRD420251271199).
Results: A total of 14 articles were included in the systematic review, 10 of which contributed 12 test-level datasets to the quantitative synthesis. For conventional US (six studies), the pooled sensitivity was 0.55 [95% confidence interval (CI): 0.36–0.73], the pooled specificity was 0.79 (95% CI: 0.73–0.84), the area under the HSROC curve (AUC) was 0.78, and the pooled PLR and NLR were 2.47 (95% CI: 1.42–4.28) and 0.54 (95% CI: 0.30–0.95), respectively. AI/radiomics models (three studies) showed a pooled sensitivity of 0.93 (95% CI: 0.80–0.98), a specificity of 0.82 (95% CI: 0.60–0.94), and an AUC of 0.94; however, likelihood-ratio CIs were wide, and the bivariate covariance estimate reached the boundary. Evidence for SWE was limited to two potentially overlapping reports, and only one multimodal combined-model study had a complete harmonized 2×2 table. At a 40% pretest probability, a negative conventional US result reduced the posttest probability of residual nodal disease to approximately 26%.
Conclusions: Conventional US alone does not reliably exclude residual axillary nodal disease after NAC. AI/radiomics approaches show promising diagnostic performance, but the evidence for this is sparse, heterogeneous, and imprecise. The currently available data do not support definitive modality ranking or omission of axillary surgery on the basis of imaging alone, and prospective multicenter external validation is required.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Liu T, He Y, Yuan X, Wang J, Liu Y, Cao C, et al. Diagnostic accuracy of ultrasound-based imaging modalities for assessing residual axillary lymph node disease after neoadjuvant chemotherapy in patients with breast cancer: a systematic review and meta-analysis. Quant Imaging Med Surg. 2026 Nov;16(11):868-8. doi:10.21037/qims-2026-1427.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
Background: Pulmonary neuroendocrine carcinomas (pNECs) are a rare and heterogeneous group of malignancies, ranging from low-grade carcinoids to high-grade small-cell lung carcinoma (SCLC) and large-cell neuroendocrine carcinoma (LCNEC). Prognostic tools for these tumors are scarce and have rarely been validated in external cohorts.
Objectives: This study aimed to develop a Transformer-based prognostic prediction model for pNEC and validate it in an independent external cohort, to improve survival estimation and support individualized treatment decisions.
Methods: This retrospective two-center cohort study included 823 patients with surgically resected pNEC (2001-2023). Models were developed in Center 1 (n=374; randomly split 8:2 into training and internal test sets) and externally validated in an independent Center 2 cohort (n=449). Three algorithms—logistic regression (LR), decision tree (DT), and the Transformer-based tabular foundation model TabPFN—were compared for estimating overall survival (OS) and disease-free survival (DFS) at 1-, 3-, and 5-year landmarks, with evaluation by receiver operating characteristic, calibration, and decision curve analyses, and SHapley Additive exPlanations (SHAP) analysis for interpretability.
Results: TabPFN achieved the highest area under the curve (AUC) in most tasks in both test sets, with task-dependent exceptions: in the internal test set, logistic regression performed best for 1-year OS (AUC 0.885), while TabPFN performed best for the remaining tasks, including 3-year OS (0.880), overall DFS (0.810), and 1-year DFS (0.892); in the external test set, TabPFN remained best for 1- and 3-year OS and for overall, 1-, and 3-year DFS (0.747-0.802), whereas logistic regression performed best for 5-year OS and DFS. External calibration was less satisfactory. Disease stage, pathological subtype, and age were the key prognostic predictors, and TabPFN was more accurate for low-grade subtypes. SHAP analysis quantified the contribution of each feature and made the model interpretable.
Conclusions: The Transformer-based model improves survival estimation in pNEC, with task-dependent advantages over conventional methods and confirmed transportability to an independent institutional cohort. Prospective multicenter validation and calibration refinement are needed before broader clinical use.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Liu L, Liang L, Wang D, Zhang J, Zhao K, Guo C, et al. Prognostic prediction of pulmonary neuroendocrine carcinoma using a transformer-based artificial intelligence model: a two-center retrospective cohort study. Front Oncol. 2026 Oct 23;16:1948262. doi:10.3389/fonc.2026.1948262.Checked: Abstract only - Abstract
Background: Bone metastasis is a common and clinically important complication of non-small cell lung cancer (NSCLC) and is associated with poor survival. Although positron emission tomography-computed tomography (PET-CT) provides comprehensive skeletal assessment, its cost and radiation exposure may limit routine use. Therefore, an accessible approach based on routinely available clinical and chest CT-related information may help identify patients at higher risk who could benefit from further skeletal imaging. This study aimed to develop an effective predictive model for bone metastasis in patients with NSCLC.
Methods: This study retrospectively analyzed data from 17,646 Asian NSCLC patients in the Surveillance, Epidemiology, and End Results (SEER) database (2000–2021) as a training set and used an independent external cohort of 308 NSCLC patients from Shanghai Chest Hospital (2022–2023) for validation. Univariate and multivariate logistic regression (LR) analyses and Collinearity analysis were performed for variable selection. A novel DualDrop-Gated Transformer (DGT) model was developed and compared with LR, extreme gradient boosting (XGBoost), LightGBM, TabTransformer, and FTTransformer. Performance was assessed by receiver operating characteristic (ROC), calibration, and decision curves.
Results: Age, sex, T stage, N stage, histological subtype, and lung metastasis status were identified as significant predicting variables. The DGT model achieved an area under the curve (AUC) of 0.947 in the training set, 0.936 in the internal test set, and 0.879 in the external validation cohort with balanced sensitivity (0.856) and specificity (0.934), significantly outperforming other models (P<0.01). Ablation experiments demonstrated the significant contributions of gating mechanisms and dropout to model performance. A nomogram based on the DGT model was constructed, with reasonable calibration, and decision curve analysis (DCA) revealed good clinical utility.
Conclusions: A novel DGT prediction model for bone metastasis in patients with NSCLC based on a highly generalizable and interpretable deep learning framework was developed and validated using 6 predictors, which may have potential application value in early screening, decision‐making, and individualized management for lung cancer patients.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Wang X, Yu H, Shen Y, Qian Q, Gao X, Xie Z, et al. A DualDrop-Gated Transformer model for predicting bone metastasis in Asian patients with non-small cell lung cancer: a real-world study with external validation. Quant Imaging Med Surg. 2026 Nov;16(11):863-3. doi:10.21037/qims-2026-0574.Checked: Abstract only
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
No new qualifying paper for this issue.
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
Accurate brain tumor segmentation from MRI images is crucial for diagnosis, treatment planning, and prognosis. In this study, we compare two fundamentally different approaches to tumor segmentation: a supervised deep learning model (2D U-Net) and an unsupervised clustering algorithm (K-means). We evaluate multiple variants of the 2D U-Net trained on different (Magnetic Resonance Imaging) MRI sequences (FLAIR and T1-Gd), as well as a fine-tuned model adapted across modalities, and compare them with K-means clustering applied separately on both modalities.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-23, retrieved 2026-10-24; not the official Clarivate IF)Reference: 1. Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, RO-077125, Măgurele-Bucharest, Romania 2. University of Bucharest, Faculty of Physics, RO-077125 Măgurele-Bucharest, Romania, BADEA RA, ORIAN AȘ, 1. Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, RO-077125, Măgurele-Bucharest, Romania 2. University of Bucharest, Faculty of Physics, RO-077125 Măgurele-Bucharest, Romania, VASILIU AA, University of Bucharest, Faculty of Physics, RO-077125 Măgurele-Bucharest, Romania, et al. Exploring supervised and unsupervised methods for brain tumor segmentation in MRI images. Rom Rep Phys. 2026 Dec 15;78(4):606-6. doi:10.59277/romrepphys.2026.78.606.Checked: Abstract only
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
- Abstract
Objective: To develop and compare radiomics, deep learning, clinical, and combined models based on multiparametric MRI radiomics and 2.5D deep learning features for preoperative risk stratification of endometrial carcinoma (EC).
Methods: This retrospective study included 246 patients with pathologically confirmed EC, with preoperative clinical and imaging data collected. Clinical data, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI) were collected. Radiomics and deep learning models were constructed based on T2WI, CE-T1WI, and their combination. A combined model integrating multiparametric radiomics features, deep learning features, and clinical parameters was subsequently developed. Model performance and clinical utility were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and the DeLong test for pairwise model comparisons. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to enhance model interpretability.
Results: Univariate and multivariate logistic regression analysis identified maximum tumor diameter, MR-DMI, BMI and ER status as independent predictive factors. Comparison of radiomics, deep learning, clinical, and combined models demonstrated that the multiparametric MRI model outperformed single-sequence models based on radiomics or deep learning, while the combined model exhibited the best predictive performance in the validation set, achieving an AUC of 0.891, accuracy of 0.86, sensitivity of 0.909, specificity of 0.765, and F1-score of 0.896. DeLong test results showed that, in the training set, the combined model had higher predictive performance than all other models, and the differences were statistically significant (P < 0.05). In the validation set, the combined model showed statistically significant improvements compared with the clinical model and the T2+CET1-Rad model (P<0.05). Decision curve analysis indicated that the combined model provided the highest clinical net benefit.
Conclusion: The combination of multiparametric MRI-based radiomics and 2.5D deep learning demonstrated favorable predictive performance in stratifying risk for EC, suggesting its potential utility as a non-invasive tool and offering a promising adjunctive approach to support personalized treatment planning and prognostic evaluation for EC patients in clinical practice.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Ouyang Y, Lin M, Cao Y, He N, Zhong X, Wu Y. Multiparametric MRI-based radiomics combined with deep learning for preoperative risk stratification of endometrial carcinoma. Front Oncol. 2026 Oct 22;16:1942153. doi:10.3389/fonc.2026.1942153.Checked: Abstract only
See all 1 Endometrial papers →
Gastric cancer: treatment, trials and AI
- Abstract
Accurate preoperative assessment of lymph node metastasis (LNM) in early gastric cancer (EGC) is critical for choosing endoscopic or surgical treatment. Most computational pathology studies rely on resection specimens obtained after curative gastrectomy, which inherently limits their preoperative relevance. Our study developed a multimodal artificial intelligence (AI) framework to estimate LNM risk using preoperative whole slide images (WSIs) of endoscopic biopsies, aiming to assist clinicians in optimizing treatment strategies before the surgery and reducing unnecessary surgical risks for patients. We developed a multimodal transformer-based model with a clinical-guided co-attention mechanism to fuse pathological features and clinical variables. To overcome the information scarcity inherent in fragmented biopsy specimens, we extracted mucosa and submucosa regions from WSIs of surgical tissues to serve as pseudo-biopsies. A curriculum learning (CL) paradigm was further employed to transition the model from idealized pseudo-biopsies to real-world biopsy samples. Through the analysis of a dataset comprising 1,099 WSIs of surgical tissues and 189 WSIs of endoscopic biopsies, our framework achieved an area under the curve (AUC) of 0.759 on WSIs of surgical tissues and an AUC of 0.727 on preoperative WSIs of endoscopic biopsies. Our findings demonstrate the feasibility of a multimodal AI framework tailored for endoscopic biopsy WSIs in preoperatively assessing LNM risk in T1 EGC. As an investigational clinical decision-support tool, this framework holds promising potential to assist clinicians in preoperative risk stratification, serving as a preliminary step toward optimizing prospective surgical management.
Journal IF-equivalent: 2.8 (OpenAlex 2-year mean citedness, value as of 2026-10-24, retrieved 2026-10-25; not the official Clarivate IF)Reference: Niu X, Wang S, Yu J, Tian M, Tang Z. Multimodal Transformer-Based Preoperative Risk Assessment of Lymph Node Metastasis in Early Gastric Cancer Using Whole Slide Images of Endoscopic Biopsies. Ann Biomed Eng. 2026 Oct 22 [Epub ahead of print]. doi:10.1007/s10439-026-04380-7.Checked: Abstract only
Liver cancer: treatment, trials and AI
- Abstract
Rationale and objectives This study aims to develop a framework integrating dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based multi-channel deep learning combined with clinical modeling for the non-invasive preoperative discrimination between hepatocellular carcinoma (HCC) and mass-forming intrahepatic cholangiocarcinoma (MF-ICC).
Methods: The internal development cohort comprised 334 surgically confirmed patients, including 231 patients with HCC and 103 with MF-ICC, and was divided at the patient level into a training cohort ( n = 233) and an internal validation cohort ( n = 101). The independent external validation cohort included 50 patients, comprising 40 with HCC and 10 with MF-ICC. We constructed separate two-dimensional deep learning models based on arterial phase, portal venous phase, and delayed phase images and then developed fusion models (2D model). A multi-channel deep learning model was further developed by directly inputting the three-phase images stacked along the channel dimension. Moreover, a clinical model was built based on independent predictors identified through multivariate analysis, which was ultimately integrated with the best-performing deep learning model. Model performance was evaluated and compared.
Results: Deep learning model directly feeding the three-phase images as a multi-channel input achieved better discrimination (area under the curve [AUC] = 0.814 in the internal validation cohort) than all single-phase and corresponding fusion models. The comprehensive 2D + multi-channel + clinical model showed the best performance for the differentiation of HCC from MF-ICC, achieving an AUC of 0.887 (95% CI, 0.819–0.954) in the internal validation cohort, and generalized well to the external validation cohort (AUC = 0.908; 95% CI, 0.811–1.000). However, no significant differences in AUCs were found among the models after correction for multiple comparisons.
Conclusion: The fusion model integrating clinical predictors with DCE-MRI multi-phase deep learning feature may provide a valuable tool for preoperatively differentiating HCC from MF-ICC.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Zhang H, Guo L, Jia F, Liu W, Zhang M, Li F, et al. Dynamic contrast-enhanced MRI deep learning model combined with clinical model for noninvasive differentiation of hepatocellular carcinoma from mass-forming intrahepatic cholangiocarcinoma. Front Oncol. 2026 Oct 23;16:1936989. doi:10.3389/fonc.2026.1936989.Checked: Abstract only - Abstract
Introduction: Clinical responses to atezolizumab plus bevacizumab (AB) therapy can vary across hepatocellular carcinoma (HCC) nodules among individual patients. This study aimed to derive and externally validate a non-invasive radiomics signature (ABRad) translated from a transcriptome signature for nodule-level prediction of AB response (ABS).
Methods: We originally derived and validated ABS using 209 and 44 pre-treatment HCC samples from the IMbrave150 and GO30140 trials, respectively. ABS was then translated into ABRad using a machine-learning approach on 59 patients with paired genome-wide transcriptome and pre-resection computed tomography imaging from surgically resected HCC. Subsequently, ABRad was externally validated using 178 HCC nodules from 55 patients treated with AB therapy.
Results: The 148-gene ABS predicted therapeutic response and progression-free survival (PFS) in the clinical trial cohorts. The final ABRad model, constructed using a random forest algorithm, was heavily weighted toward contrast-enhanced features. In the external validation cohort, ABRad demonstrated a significant positive association with the degree of therapeutic response (p <0.001). The areas under receiver operating characteristics curve for predicting complete response (CR), objective response (OR), and disease control (DC) were 0.71 (95% confidence interval [CI], 0.61-0.80), 0.65 (95% CI, 0.56-0.73), and 0.69 (95% CI, 0.59-0.78), respectively. Nodule-level ABRad was independently associated with therapeutic response with adjusted odds ratios of 3.05 (95% CI, 1.23-7.57) for CR, 2.24 (95% CI, 1.06-4.77) for OR, and 2.79 (95% CI, 1.07-7.29) for DC. Patient-level ABRad was significantly associated with prolonged PFS (p = 0.005).
Conclusions: ABRad successfully predicted nodule-level response to AB therapy in HCC.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Supplemental Material for: A radio-transcriptome signature for nodule-level prediction of response to atezolizumab plus bevacizumab in hepatocellular carcinoma. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34754340.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Patients with muscle-invasive bladder cancer (MIBC) typically have a dismal prognosis, even after undergoing radical cystectomy (RC). To address this limitation, we constructed and evaluated a preoperative deep learning (DL) model that leverages computed tomography (CT) imaging to stratify MIBC patients according to their risk of postoperative disease progression. Data from MIBC patients undergoing RC at one of four institutions were retrospectively analyzed and allocated to training and validation cohorts. A dual-channel information fusion DL model was developed to predict the postoperative risk of progression according to preoperative CT images. Subsequently, a DL-based clinical signature was created by combining clinical features with the DL-derived score. The concordance index (C-index) and area under the curve (AUC) were used to evaluate the DL model's predictive capability, which was compared with the DL-based clinical signature, a clinical model, and a Ki67 model. In the retrospective cohort of 427 patients with MIBC, the DL score demonstrated favorable predictive performance for disease progression in the validation cohort (C-index: 0.774; AUC: 0.785), and generally showed higher predictive performance than the comparator models in the validation cohort. Using an optimal threshold, the patient cohort was categorized into two distinct subgroups, high-risk and low-risk, with differences in the cumulative incidence of progression observed between the two groups, supporting the model's ability to stratify patients according to progression risk. The utilization of preoperative CT imaging in a DL framework enabled PFS prediction and risk stratification in patients with MIBC, with potential value for individualized postoperative surveillance.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Liu Y, Xu Z, Xia J, Guo L, Hou W, Qu B, et al. An interpretable contrast-enhanced CT-based cross-attention transformer model for predicting progression-free survival in muscle-invasive bladder cancer: a multicenter study. Abdom Radiol. 2026 Oct 22 [Epub ahead of print]. doi:10.1007/s00261-026-05837-y.Checked: Abstract only