AI and Cancer Research ── 2026-10-09
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（2） · Breast（2） · Lung（2） · Pancreatic（2） · Prostate（1） · ACC（0） · Brain（1） · Ovarian（0） · Endometrial（0） · Gastric（1） · Liver（0） · Kidney（0） · Bladder（0）
Cancer molecular biology and AI
- Abstract
Purpose: Parenchyma-sparing hepatectomy planning depends on accurate resection zones that preserve functional liver tissue without compromising oncological margins. This work investigates how different levels of anatomical and functional information complexity influence resection zone prediction for parenchyma-sparing surgical planning for primary liver cancer.
Methods: We compare three modeling paradigms: a geometric distance-based approach, an explicit perfusion-based method using vascular anatomy, and a deep learning-based model built on the U-Net architecture. All methods operate on segmentation-derived representations of the liver, tumor, and vessels. Performance is evaluated using overlap- and distance-based metrics.
Results: The distance-based model produces predictions with limited surface deviation (HD $$_{95}$$ 95 33.89 mm) but lower overlap due to undersegmentation (DSC 58.18 %). The perfusion-based method achieves a favorable balance between overlap (DSC 67.41 %) and boundary accuracy (HD $$_{95}$$ 95 37.92 mm) but tends to overestimate the predicted region due to strict binary perfusion assumptions. The deep learning model attains the highest overlap accuracy (DSC 76.31 %) while exhibiting larger distance errors (HD $$_{95}$$ 95 65.21 mm), reflecting localized boundary inaccuracies.
Conclusion: None of the models is universally outperforming the other two for parenchyma-sparing resection planning. Deep learning shows strong predictive performance, particularly for larger resection volumes, while geometric and perfusion-based models offer greater interpretability and clinical controllability. The distance-based approach is well suited for maximal parenchyma-sparing resections, whereas perfusion-based modeling is advantageous for tumors near vessels with potential perfusion loss. Our results highlight variations due to different surgical strategies and can thus provide valuable guidance for individual patient’s surgery planning.
Journal IF-equivalent: 3.0 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Rothert J, Rakshit J, Salz JL, Huettl F, Ehses V, Huber T, et al. Resection zone prediction for parenchyma-sparing hepatectomy planning: a comparative study of three modeling paradigms. Int J CARS. 2026 Oct 7 [Epub ahead of print]. doi:10.1007/s11548-026-03798-7.Checked: Abstract only - Abstract
Cervical and lung cancers remain among the leading causes of cancer-related mortality worldwide.This highlights the need for accurate, scalable, and computationally efficient computer-aided diagnostic systems.Deep learning has shown remarkable success in histopathological image analysis.However, most existing approaches depend on large annotated datasets, extensive computational resources, and end-to-end supervised training.To address these limitations, this study proposes AJAD-BoostNet (Adaptive Joint Analysis and Deep Boosting Network).This novel hybrid framework integrates deep transfer learning, unsupervised clustering, dimensionality reduction, and boosting-based refinement for automated cancer histopathological image classification.The proposed framework uses a pretrained ResNet50 model to extract high-level semantic representations from histopathological images.Then, Principal Component Analysis (PCA) reduces feature dimensionality while preserving discriminative information.The optimized feature space is clustered with the K-Means algorithm for initial unsupervised classification.To further enhance predictive performance and correct clustering errors, an XGBoost-based boosting module is applied as a misclassification refinement strategy.The framework was evaluated on two benchmark datasets: the IARC Cervical Cancer Image Bank and the LC25000-based Lung Cancer Histopathological Images dataset.Experimental results demonstrate the effectiveness of the proposed approach across both datasets.For lung cancer classification, AJAD-BoostNet achieved an accuracy of 97.08%, an F1-score of 97.00%, a Cohen's Kappa score of 94.17%, and an AUC of 97.08%.For cervical cancer classification, the framework attained an accuracy of 64.84%, an F1-score of 65.00%, a Cohen's Kappa score of 29.20%, and an AUC of 64.55%.These results surpass those of conventional CNN-based models trained under similar conditions.Additionally, 10-fold cross-validation and confidence interval analyses confirmed the robustness, stability, and generalization capability of the proposed framework.The findings indicate that combining deep semantic feature extraction with clustering and boosting provides an efficient and interpretable alternative to fully supervised deep learning architectures.This makes AJAD-BoostNet a promising solution for intelligent cancer diagnosis in resource-constrained clinical environments.
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]. AJAD-BoostNet: A Hybrid Deep K-Means and XGBoost Framework for Automated Cancer Histopathological Image Classification. IJDDT. 2026 Oct 7;16(57s). doi:10.25258/ijddt.16.57s.160.Checked: Abstract only
See all 2 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
Manual data entry and curation is the most used method of data collection for real world evidence and clinical trials but is a time-consuming task associated with significant human effort and transcription mistakes. MIRROR is a retrospective study that compared manual data capture from electronic case report forms (eCRF) to automated data extraction from electronic health records (EHRs) using artificial intelligence (AI). Clinical information was extracted from EHRs of 113 breast cancer patients participating in 11 clinical trials, with the system providing references to the original text for each variable to enable efficient verification. All patients were enrolled at the Virgen del Rocío University Hospital (Sevilla, Spain). Analyses provided high rates of accuracy, precision, recall and F1-score for most structured and unstructured clinical data domains. Future efforts should focus on improving unstructured data extraction and implementing standardized eCRF among sites to ensure consistent extraction and evaluation of clinical information.
Journal IF-equivalent: 6.2 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Parra-Calderón CL, Mina L, Ruiz-Borrego M, García J, Domínguez CD, Guich J, et al. Artificial intelligence for clinical data extraction from EHRs in breast cancer trials: the MIRROR study. npj Breast Cancer. 2026 Oct 7 [Epub ahead of print]. doi:10.1038/s41523-026-01060-6.Checked: Abstract only - Abstract
ABSTRACT Accurate preoperative assessment of breast lesion malignancy and axillary lymph node (ALN) status is important for individualized breast cancer management, yet current approaches are limited by diagnostic variability and invasive nodal staging. We aimed to develop and validate a multicenter artificial intelligence framework for breast lesion classification and ALN metastasis prediction using dynamic contrast‐enhanced magnetic resonance imaging (MRI) and preoperative clinical information. In this multicenter study, 3320 patients from four hospitals were assigned to a training cohort, an internal testing cohort, and two independent external validation cohorts. Lesion classification was based on MRI alone, whereas ALN prediction additionally incorporated clinical characteristics. MRI sequences were analyzed using a Video Swin Transformer Tiny backbone with low‐rank adaptation and attention‐based multiple‐instance learning, with gated multimodal fusion used for ALN prediction. The model achieved AUCs of 1.000, 1.000, 1.000, and 0.994 for lesion classification and 0.991, 0.974, 0.987, and 0.960 for ALN prediction across the four cohorts. Calibration and decision curve analyses further characterized model performance, while exploratory survival analysis showed that AI‐predicted ALN status was associated with disease‐free survival. These findings support the potential of AI‐assisted breast MRI to provide quantitative information for breast lesion characterization and preoperative assessment of ALN involvement.
Journal IF-equivalent: 5.4 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Ren W, He Z, Deng Z, Huang Z, Cai G, Ban X, et al. Cross‐Modal Artificial Intelligence Integrating Dynamic MRI and Clinical Data for Breast Cancer Diagnosis and Nodal Metastasis Prediction. MedComm – Oncology. 2026 Oct 6;5(4):e70100. doi:10.1002/mog2.70100.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
Objective: To develop and validate an interpretable machine learning–based model for the early identification of the risk of clinical deterioration during hospitalization in newly diagnosed lung cancer patients.
Methods: Clinical data of patients with newly diagnosed lung cancer admitted between July 2023 and December 2025 were retrospectively collected in this single-center study. A hybrid feature selection strategy integrating least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm was employed to identify key predictors. Logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) models were constructed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values were further applied to interpret the model's decision-making process.
Results: A total of 1,001 newly diagnosed lung cancer patients were included, comprising 121 patients (12.1%) in the deterioration group and 880 patients (87.9%) in the non-deterioration group. Through the hybrid feature selection approach, five core predictive variables were identified, including Activities of Daily Living (ADL) score, lymphocyte count, C-reactive protein (CRP), respiratory rate, and neutrophil count. Among the models, the SVM model achieved the highest observed AUC of 0.822 (95% CI: 0.737–0.899) in the internal validation set, with a sensitivity of 0.750 (95% CI: 0.594–0.893) and a specificity of 0.792 (95% CI: 0.740–0.838). After Platt scaling calibration, the model achieved a Brier score of 0.083 (Hosmer-Lemeshow p = 0.649), with a calibration intercept of 0.141 and a slope of 1.126, indicating satisfactory agreement between predicted and observed risks. DCA suggested a potential net clinical benefit. SHAP analysis, in conjunction with multivariable regression results, consistently demonstrated that ADL score and lymphocyte count were associated with reduced risk of clinical deterioration, whereas CRP and neutrophil count were associated with increased risk.
Conclusion: The SVM model developed in this study may help identify patients at risk of clinical deterioration among hospitalized lung cancer patients and may assist healthcare professionals in achieving early risk identification and potential clinical decision support.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Li H, Li Y, Huang X, Zhang Z, Liu Q, Chen Q. Development and internal validation of a predictive model for clinical deterioration in newly diagnosed lung cancer patients based on interpretable machine learning. Front Med. 2026 Oct 7;13:1947529. doi:10.3389/fmed.2026.1947529.Checked: Abstract only - Abstract
Objectives: Survival extrapolation is a consequential choice in oncology cost-effectiveness analysis (CEA), and conventional parametric approaches have known limitations under non-proportional hazards, common with immune checkpoint inhibitors. This study tested whether machine learning (ML) survival models offer a useful alternative to parametric extrapolation using KEYNOTE-189 data, and quantified how survival-model choice affects the incremental cost-effectiveness ratio (ICER) of first-line pembrolizumab plus chemotherapy for advanced non-small cell lung cancer (NSCLC) from an Indian healthcare perspective.
Methods: Individual patient-level data were reconstructed from published Kaplan–Meier curves using the Guyot algorithm. Five parametric distributions and two ML approaches (random survival forest [RSF] and penalised Cox regression with elastic-net regularisation) were fitted and compared on a held-out test set using Harrell’s C-statistic and the integrated Brier score (IBS). A three-state Markov model used survival-derived transition probabilities, Indian drug-acquisition costs, and published EQ-5D utility values to estimate lifetime costs and quality-adjusted life-years (QALYs), with uncertainty assessed via probabilistic sensitivity analysis (PSA), deterministic sensitivity analysis, and expected value of perfect information (EVPI).
Results: The RSF achieved a C-statistic of 0.641 and IBS of 0.1748, comparable to the best-performing parametric models (log-logistic, log-normal) and modestly ahead of the conventionally selected Weibull distribution (C = 0.638). The base-case ICER was ₹1,05,51,767 per QALY with Weibull-derived transitions versus ₹97,96,723 with RSF-derived transitions, a 7.2% reduction. Across 10,000 PSA iterations, the probability of cost-effectiveness at a ₹5,00,000/QALY threshold was near zero under both models; population EVPI was negligible at that threshold but peaked near ₹34,773 crore around ₹1.06 crore/QALY. Drug acquisition cost was the dominant driver of ICER uncertainty, with survival-model choice ranking third among seven parameters examined.
Conclusion: Machine learning survival modelling produced a modest but consistent improvement in predictive accuracy over standard parametric distributions, translating into a non-trivial reduction in the estimated ICER. The reimbursement conclusion that pembrolizumab plus chemotherapy is not cost-effective at prevailing Indian prices was robust to survival-model choice, though the price reduction needed to reach cost-effectiveness varied by approach. Survival-model choice merits explicit consideration as a structural uncertainty in oncology health technology assessment, particularly for therapies with non-proportional hazard patterns.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: [No authors listed]. Comparative Evaluation of Machine Learning and Parametric Survival Modelling in Cost-Effectiveness Analysis: A Case Study of First-Line Pembrolizumab Plus Chemotherapy for Non-Small Cell Lung Cancer in India. Journal of chemical health risks. [Epub ahead of print]. doi:.Checked: Abstract only
Pancreatic cancer: treatment, trials and AI
- Abstract
Pancreatic ductal adenocarcinoma comprises classical and basal-like molecular subtypes that differ in prognosis and treatment response. However, transcriptomic subtyping is limited by cost, turnaround time, and tissue requirements. We aimed to determine whether these molecular phenotypes can be inferred directly from routine hematoxylin and eosin–stained whole-slide images using deep learning. We analyzed 689 patients with paired histology and RNA-sequencing data from the Pancreatic Cancer Action Network cohort (n = 506) and The Cancer Genome Atlas (n = 183). Molecular subtypes were defined using the Moffitt 50-gene signature refined by GATA6 expression. PanSubNet integrates cellular morphology with tissue architecture to predict subtype from histology. The model was trained and internally evaluated using five-fold cross-validation on 171 high-confidence cases and independently evaluated on 75 high-confidence external cases. Performance was assessed using classification metrics, and survival associations were evaluated using Kaplan–Meier analysis and log-rank testing. Here we show that PanSubNet distinguishes high-confidence classical and basal-like tumors with a mean area under the receiver operating characteristic curve of 90.3% in internal validation and 84.0% in independent external validation. The model also captures features associated with intermediate transcriptional states. In metastatic disease, PanSubNet-predicted subtypes significantly stratify overall survival and identify aggressive tumors among transcriptionally discordant cases. PanSubNet enables molecular subtyping directly from routine histology and provides a rapid, tissue-sparing complement to transcriptomic profiling. This approach may broaden access to biologically informed stratification, particularly when molecular testing is limited or impractical. Pancreatic cancer can be divided into biological groups that may differ in how aggressive the disease is and how patients respond to treatment. These groups are usually identified using gene-expression testing, which can be expensive, slow, and difficult when only a small amount of tissue is available. We developed PanSubNet, an artificial intelligence method that predicts these biological groups directly from routine microscope slides. We trained and tested the model using patients with both tissue slides and gene-expression data from two independent datasets. PanSubNet accurately identified the major pancreatic cancer subtypes and also captured features related to tumors with intermediate biology. This approach could provide faster and more accessible tumor profiling and may support future treatment planning when molecular testing is limited or unavailable.
Journal IF-equivalent: 6.0 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Akbar AR, Leyva A, Esnakula A, Hasanov E, Noonan A, Meng L, et al. Inferring clinically relevant molecular subtypes of pancreatic cancer from routine histopathology using deep learning. Commun Med. 2026 Oct 6 [Epub ahead of print]. doi:10.1038/s43856-026-01952-5.Checked: Abstract only - Abstract
Accurately predicting the risk of early liver metastases (ELM) and identifying patients who are most likely to benefit from neoadjuvant therapy (NAT) are critical for pancreatic ductal adenocarcinoma (PDAC). Here, we develop a Mamba-based predictive model that integrates imaging features from both the primary pancreatic tumor and the liver to assess the risk of ELM. The model is evaluated in a multi-institutional cohort of 1063 PDAC patients and demonstrates robust performance in predicting ELM (AUCs: 0.806-0.890). Besides, model-defined high-risk patients exhibit significantly shorter progression-free survival (PFS: HR = 1.93, p < 0.001) and overall survival (OS: HR = 1.89, p < 0.001). Notably, NAT confers significant OS benefits in model-defined high-risk patients (17.4 vs. 34.1 months; p < 0.001), even after propensity score matching (p = 0.004), while no survival benefit occurs in low-risk patients. Radiotranscriptomic analyses further reveal relative biological aggressiveness in the high-risk group. Overall, our proposed Mamba-based framework enables accurate prediction of ELM and may serve as a clinically actionable tool for identifying PDAC patients most likely to benefit from NAT.
Journal IF-equivalent: 18.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Zhao B, Gong Z, Xiao W, Chen M, Huang S, Zhu L, et al. Deep learning CT signature for predicting early liver metastases in pancreatic ductal adenocarcinoma. Nat Commun. 2026 Sep 8;17(1):10596. doi:10.1038/s41467-026-77665-z.Checked: Abstract only
Prostate cancer: treatment, trials and AI
- Abstract
Prostate cancer remains one of the most prevalent malignancies among men worldwide. Effective post-diagnostic management requires repeated staging and longitudinal risk assessment. However, existing clinical decision support systems (CDSS) rely predominantly on structured and resource-intensive clinical data, which limits their scalability and accessibility outside conventional care settings. This study presents a resource-efficient multimodal AI framework for post-diagnostic prostate cancer staging and advanced-stage risk stratification for home-based monitoring. The system integrates potentially home-accessible inputs, including numerical, categorical, and unstructured textual features, systematically selected from the Cancer Screening Trial dataset through supervised machine learning and is implemented as a standalone, user-facing application. The dual-mode framework consists of a comprehensive model for multiclass stage classification and a minimal model for binary advanced-stage risk stratification using a reduced feature set, balancing predictive performance with resource efficiency. The CDSS demonstrates discriminatory performance in a retrospective analysis of the Prostate, Lung, Colorectal and Ovarian cancer screening trial dataset (ROC-AUC 0.95), while cross-dataset evaluation of a feature-restricted model using the Surveillance, Epidemiology, and End Results dataset showed an ROC-AUC of 0.86 under limited feature overlap. The minimal model has a lightweight implementation with a serialized size of 9.51 MB and sub-50-ms CPU inference latency, supporting real-time deployment in resource-constrained settings. Model interpretability is supported through SHapley Additive exPlanations, while an uncertainty thresholding mechanism enables the system to flag low-confidence predictions for expert review. This work presents a patient-facing prototype for oncology risk assessment, with potential for future evaluation in resource-constrained, decentralized, and home-based care settings.
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: Khan AS, Tabassum S, Nag AK, Majumder S. Resource-efficient multimodal AI for post-diagnostic prostate cancer risk stratification for home-based monitoring. Sci Rep. 2026 Oct 7 [Epub ahead of print]. doi:10.1038/s41598-026-75102-1.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
Background: Current preoperative assessment of meningioma brain invasion (BI) demands imaging analytical frameworks that optimize computational efficiency while capturing multidimensional tumor-pathology correlations. Building upon the inherent advantages of 2.5D MRI in synthesizing multiplanar biological data with manageable processing loads, this study innovatively integrates deep transfer learning (DTL), a technique bridging pretrained network knowledge with target domain adaptation, to systematically identify tumor-invasive biological signatures and develop an optimized predictive system using preoperative contrast-enhanced T1-weighted imaging (CE-T1WI).
Methods: This retrospective study enrolled 674 patients with pathologically confirmed meningiomas, including 53 with BI and 621 without BI. The cohort was stratified into a training set and an independent test set. Tumor regions of interest (ROIs) were delineated on CE-T1WI, and radiomics and DTL features were extracted. Radiomics and DTL features were fused via early fusion, followed by feature selection. Within the training cohort, five-fold cross-validation was performed, with SMOTE applied exclusively to the training portion of each fold; validation folds and the independent test set retained their original class distributions.
Results: The effectiveness of the models was compared using the area under the ROC curve (AUC). In the training set, ComM achieved an AUC of 0.977, while in the test set, its AUC reached 0.935. ComM demonstrated the best performance (training set: AUCComM > AUCDTLRM > AUCRadM > AUCClinM > AUCDTLM; test set: AUCComM > AUCDTLRM > AUCRadM > AUCDTLM > AUCClinM), showing excellent preoperative predictive capability for meningioma BI. The Hosmer-Lemeshow test indicated good model fit, and the calibration curve revealed that ComM was closest to the ideal curve.
Conclusion: ClinM, RadM, DTLM, DTLRM, and ComM based on 2.5D CE-T1WI all exhibited good performance in predicting meningioma BI. The clinical-radiomics-DTL fusion model (ComM) demonstrated the highest efficacy, suggesting that DTL provides additional features correlated with meningioma BI that differ from radiomics features. This model shows potential clinical utility for preoperative prediction; however, further external validation in larger multicenter cohorts is required before clinical implementation.
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: Yuan D, Zhang J, Zhang C, Jing X, Feng Q, Han T. Non-invasive preoperative MRI-based deep transfer learning radiomics model for predicting meningioma brain invasion. Front Oncol. 2026 Oct 7;16:1935311. doi:10.3389/fonc.2026.1935311.Checked: Abstract only
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
No new qualifying paper for this issue.
Gastric cancer: treatment, trials and AI
- Abstract
PURPOSE: Peritoneal metastasis from gastric cancer (GC) is generally considered incurable. Although conversion surgery is the preferred strategy, surgeons frequently encounter clinical scenarios requiring upfront surgery. Currently, objective criteria for predicting prognosis after upfront R0 gastrectomy remain undefined. MATERIALS AND METHODS: This retrospective multicenter cohort study analyzed data from 792 patients derived from the PASS-META cohort and an independent validation dataset collected between 2014 and 2021. The final analysis included 80 patients with peritoneal oligometastasis (P1/P2) who underwent upfront R0 gastrectomy for risk stratification, and 632 controls who underwent R2 gastrectomy or no gastrectomy for survival comparison. A component-wise gradient-boosting survival model was developed and simplified into an 8-item risk scoring system. RESULTS: The machine learning (ML) model demonstrated robust discrimination in external validation (concordance index, 0.811; 95% confidence interval, 0.677-0.955) and stratified patients into low- and high-risk groups with significantly different survival outcomes (log-rank P<0.001), independent of systemic chemotherapy compliance. Low-risk patients achieved a 5-year survival rate exceeding 30%, whereas high-risk patients showed survival rates comparable to those of patients who underwent R2 gastrectomy or no gastrectomy. The simplified 8-item risk groups showed high concordance with the ML model risk groups (F1-score, 0.975). CONCLUSIONS: We developed and validated a prediction model and an 8-item risk scoring system that effectively stratified patients with peritoneal oligometastatic GC according to their prognosis after upfront R0 gastrectomy. Although prospective validation is warranted, this framework provides objective prognostic guidance that may support intraoperative decision-making, distinguishing patients with favorable long-term outcomes from those whose survival is comparable to that of noncurative approaches.
Journal IF-equivalent: 5.8 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Lee S, Roh YH, Shin HJ, Song JH, Kim SE, Lee IS, et al. Prognostic Model for Survival After Upfront R0 Gastrectomy in Peritoneal Oligometastatic Gastric Cancer. J Gastric Cancer. 2026;26(4):516. doi:10.5230/jgc.2026.26.e40.Checked: Abstract only
Liver cancer: treatment, trials and AI
No new qualifying paper for this issue.
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
No new qualifying paper for this issue.