AI and Cancer Research ── 2026-10-10
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（1） · Prostate（2） · ACC（0） · Brain（0） · Ovarian（0） · Endometrial（0） · Gastric（1） · Liver（2） · Kidney（0） · Bladder（0）
Cancer molecular biology and AI
- Abstract
Brain metastasis represents the most prevalent central nervous system tumor among adults and is associated with an unfavorable prognosis and reduced overall survival rates. A proportion of patients present with brain metastasis as the first manifestation of an unidentified primary tumor. However, conventional qualitative MRI evaluation remains inadequate for precisely identifying the primary origin of brain metastases. Therefore, this systematic review and diagnostic meta-analysis aimed to evaluate the diagnostic performance of MRI-based artificial intelligence models for predicting the primary tumor origin of brain metastases and to identify factors contributing to variability in their performance.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. MRI-based artificial intelligence for primary tumor origin prediction in brain metastases: a systematic review and diagnostic meta-analysis. Open Science Framework. [Epub ahead of print]. doi:10.17605/osf.io/kz95c.Checked: Abstract only - Abstract
Trophoblast cell surface antigen 2 (TROP-2) is a transmembrane glycoprotein overexpressed across a range of epithelial malignancies, yet its clinical assessment still relies largely on invasive immunohistochemical analysis of biopsied tissues. In this study, we employed the PepMimic artificial intelligence platform to de novo design a TROP-2-targeting peptide, TR23, via binding interface mimicry, which was subsequently conjugated with DOTA and radiolabeled with 68Ga to yield the peptide-based PET probe [68Ga]Ga-DOTA-TR23. The tracer bound TROP-2 with nanomolar affinity (KD = 26.8 nM), showed selective cellular uptake in TROP-2-positive cells, and exhibited high radiochemical purity (>95%) with excellent stability in saline and serum. Micro-PET/CT imaging in pancreatic (BxPC-3), prostate (PC3), and thyroid (BCPAP) xenograft models revealed rapid, specific tumor accumulation with high contrast and low background uptake, while competitive blocking studies with excess unlabeled TR23 substantially suppressed tumor uptake, confirming receptor-mediated specificity. Quantitative analysis further demonstrated strong positive correlations between SUVmax and TROP-2 expression levels verified by immunohistochemistry across all three tumor types. Collectively, these findings establish [68Ga]Ga-DOTA-TR23 as a promising peptide-based PET tracer for rapid, specific, and quantitative imaging of TROP-2-positive tumors, and highlight the PepMimic-enabled strategy as a versatile platform for peptide-based probe discovery, supporting the translational potential of this tracer as a pan-cancer diagnostic agent for precision molecular imaging.
Journal IF-equivalent: 3.7 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Lin Z, Yang Z, Ma S, Lin X, Zhang Q, Miao W, et al. De Novo Design of a TROP-2-Targeting Peptide PET Tracer via AI-Guided Binding Interface Mimicry. Bioconjugate Chemistry. 2026 Oct 8 [Epub ahead of print]. doi:10.1021/acs.bioconjchem.6c00394.Checked: Abstract only
See all 2 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
Background: This study evaluates DL using GEX and preoperatively available clinical data (PreopClinic) to predict SLNM, and explores their potential for guiding axillary surgery and prognostic assessment.
Methods: We retrospectively included 6,836 clinically node-negative T1-T2 patients with invasive breast cancer who underwent primary surgery from the SCAN-B. Three DL models—a multilayer perceptron, a pathway-informed sparse neural network, and a transformer—were developed using the development set (n=4,625) and evaluated against XGBoost in the independent test set (n=2,211).
Results: The Transformer outperformed other methods for GEX modeling and minimized prior gene selection. In the independent test set, the combined Pre-opClinic+GEX model significantly improved SLNM prediction compared to Pre-opClinic alone (ROC AUC 0.693 vs 0.596, P < 0.001) and identified low-risk patients who might avoid unnecessary SLNB (reduction rate 27.2% at 92.1% sen-sitivity). However, the combined model did not significantly outperform GEX alone. While GEX provided the dominant predictive signal, PreopClinic contributed complementary information with modest numerical gains in clinical utility. Across-subtype training outperformed within-subtype training, particularly in TNBC, where the combined model achieved AUC 0.734 (95% CI: 0.644-0.837). The derived SLNM predictor also provided prognostic information beyond the estab-lished prognostic factors. Although the models were developed primarily using surgical specimen–derived GEX, paired biopsy and surgical-specimen analyses (n=116) demonstrated substantial concordance of transcriptomic patterns and nodal predictions.
Conclusion: These findings highlight the Transformer’s robustness against noise and effectiveness in capturing informative transcriptomic features for SLNM pre-diction. The agreement observed between paired biopsy and surgical specimens supports the feasibility of future biopsy-based preoperative applications.
Journal IF-equivalent: 2.2 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Zhang D, Staaf J, Bendahl PO, Dihge L, Ohlsson M, Sjöström M, et al. Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort. Clinical Cancer Research. 2026 Oct 8 [Epub ahead of print]. doi:10.1158/1078-0432.ccr-26-1134.Checked: Abstract only - Abstract
Background: Breast cancer patients receiving neoadjuvant treatment may benefit from a presurgical assessment of complete response to avoid unnecessary surgeries. We aimed to develop a machine learning model that predicts pathological complete response (pCR) based on results from different image modalities as well as tumor and patient characteristics.
Methods: Clinical data from 388 cases of invasive breast cancer receiving neoadjuvant treatment were extracted in this retrospective, monocentric study. Findings from ultrasound, mammography and magnetic resonance imaging combined with patient and histopathological information were included. A Pearson Product-Moment correlation and a LASSO regularization were performed to preselect the most relevant features for the outcome. Based on the preselected variables, we developed and validated a machine learning algorithm (logistic regression with elastic net penalty (GLM)) to predict pCR. The model’s performance was evaluated via area under the curve (AUROC), false positive rate (FPR) and positive predictive value (PPV).
Results: In the total cohort, pCR was detected in 158 out of 388 cases, resulting in a pCR rate of 40.7%. From 111 collected variables, 8 variables were removed due to a high correlation with other features. After the LASSO regularization, 14 variables were included as predictors for the final model. In the validation set, the GLM achieved an AUROC of 0.76 with a FPR of 8.7% and a PPV of 75%.
Conclusions: Through the combination of imaging and clinicopathological features, our machine learning model shows a promising approach to improve the prediction of pCR in breast cancer patients receiving neoadjuvant treatment. In combination with the LASSO regularization, it allows interesting insights into the predictive potential of different variables, which are available as part of standard clinical practice. Trial registration Not applicable.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Reisig E, Cai L, Müller M, Schäfgen B, Pfob A. Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer. BMC Cancer. 2026 Oct 8 [Epub ahead of print]. doi:10.1186/s12885-026-17115-7.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
Background. Detecting acquired epidermal growth factor receptor (EGFR) T790M after resistance to first- or second-generation EGFR tyrosine kinase inhibitors (EGFR-TKIs) can guide subsequent treatment. We developed and temporally validated a multimodal computed tomography (CT) model to predict T790M detected at progression. Methods. This retrospective study included 198 patients with lung adenocarcinoma treated with first- or second-generation EGFR-TKIs. Patients were allocated chronologically to a development cohort (n = 139) and a temporal validation cohort (n = 59). Clinical variables, CT semantic features, handcrafted radiomics, and deep features extracted with a 2.5-dimensional ResNet18 network were combined using elastic-net logistic regression. A dynamic model also included early treatment response and delta-radiomic features. Results. The baseline multimodal model achieved areas under the receiver operating characteristic curve (AUCs) of 0.881 and 0.856 in the development and validation cohorts, respectively. The corresponding AUCs for the dynamic model were 0.914 and 0.889. In temporal validation, the dynamic model had a sensitivity of 0.867 and a specificity of 0.862. Its AUC was numerically higher than that of the baseline model, but the difference was not statistically significant (P = 0.184). Higher predicted T790M probability was associated with EGFR exon 19 deletion, greater early tumor shrinkage, and longer progression-free survival. Conclusions. Combining clinical and CT-derived features showed promise for predicting T790M detected at progression after first- or second-generation EGFR-TKI therapy. Early response and delta-radiomics provided a numerical improvement in discrimination. Prospective multicenter validation is needed before this approach can inform molecular retesting in clinical practice.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Noninvasive prediction of acquired EGFR T790M in lung adenocarcinoma using multimodal CT radiogenomics. Zenodo (CERN European Organization for Nuclear Research). [Epub ahead of print]. doi:10.5281/zenodo.23249913.Checked: Abstract only - Abstract
The medicinal fungus Ganoderma lucidum is rich in bioactive triterpenoids and polysaccharides. The complexity and adaptive resistance of lung cancer necessitate therapeutic strategies that simultaneously disrupt multiple oncogenic pathways. We employed a systems pharmacology approach that integrates network analysis and artificial intelligence to elucidate the multitarget anticancer mechanisms of G. lucidum compounds. Bioactive metabolites were screened, and their targets were integrated with lung cancer-associated genes to construct a compound-target-pathway network. AI-based molecular docking validated key interactions. The top-predicted multitarget compounds were functionally validated in H1299 nonsmall cell lung cancer cells using viability, migration, and Western blot assays. A network of 67 synergistic targets, including PIK3CA, STAT3, EGFR, and TP53, concurrently modulates proliferation, apoptosis, and immune evasion pathways. Triterpenoids, such as lucidumol A and ganoderic acid A, act as key drivers capable of binding multiple signaling hubs. These metabolites and a standardized G. lucidum extract synergistically disrupt oncogenic signaling by inhibiting cell proliferation (extract IC 5 0 = 1669 μg/mL), suppressing migration, and reducing STAT3 phosphorylation and c-Myc expression. These fungal metabolites exhibit potent multitarget tumor-suppressive properties.
Journal IF-equivalent: 5.5 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Wang H, Chen L, Zhang S, Ju M, Feng J, Tang Z, et al. AI-assisted systems pharmacology with experimental validation reveal multi-target anticancer mechanisms of Ganoderma lucidum extracts against lung cancer. Mycology. 2026 Mar 6;17(3):773-93. doi:10.1080/21501203.2026.2634522; PMCID: PMC13647369.Checked: Full text checked
Pancreatic cancer: treatment, trials and AI
- Abstract
Cancer cell classification over intermediate or hybrid states during the epithelial‐to‐mesenchymal transition (EMT) provides information on their heterogeneity, plasticity, and invasiveness. While accomplished by immunofluorescence imaging of protein markers, it is low‐throughput and limited by sample heterogeneity. Since intracellular redistribution of filamentous proteins during EMT alters cellular biomechanics, we present single‐cell imaging over a continuum of deformation and recovery regions under microfluidic viscoelastic flows coupled to a multi‐region deep learning framework for high‐throughput EMT classification on morphometric shape descriptors, binary masks of cell geometry, and brightfield images retaining intracellular texture. We infer that despite nuclear enlargement during EMT that enhances stiffness, vimentin redistribution around the cytoskeleton likely allows progressively EMT‐induced pancreatic cancer cells to support greater deformation and faster relaxation to isotropic shapes. Using an architecture combining convolutional feature extraction with attention‐based regional aggregation, the contributions of cell deformation and relaxation toward classification of intermediate EMT states are captured using global geometric information and intracellular texture related to cytoskeletal remodeling. In comparison to single‐region imaging flow cytometry or scalar deformability metrics, the reported multi‐region attention‐based measurement of the deformation and relaxation dynamics improves the classification of intermediate EMT states that present the greatest plasticity for metastasis.
Journal IF-equivalent: 5.6 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Jarmoshti J, Gao H, Zeinali N, Siddique A, Adair SJ, Bauer TW, et al. Deep Learning Classification of Pancreatic Cancer Cells Over a Progression of Epithelial to Mesenchymal States by Deformability Cytometry Under Microfluidic Viscoelastic Flows. Advanced Intelligent Systems. 2026 Oct 7 [Epub ahead of print]. doi:10.1002/aisy.70573.Checked: Abstract only
Prostate cancer: treatment, trials and AI
- Abstract
Accurate patient-level risk stratification of clinically significant prostate cancer (csPCa) on biparametric MRI remains challenging, as most existing approaches require costly lesion-level annotations or have not been evaluated in large temporally separated cohorts. We developed and validated a multimodal deep learning framework combining T2-weighted (T2w), diffusion-weighted (DWI), and apparent diffusion coefficient (ADC) volumes with age, prostate-specific antigen (PSA), and PSA density (PSAD) for patient-level csPCa classification, trained without lesion-level annotations. The study included a retrospective development cohort of 3, 939 examinations and an independent temporal validation cohort comprising 1, 409 prospectively acquired examinations from 13 centers. The model employs three sequence-specific encoders with asymmetric cross-attention fusion and a two-stage transfer-learning strategy. Adding demographic and clinical variables consistently improved performance over MRI-only models, with PSAD emerging as the most informative complementary factor, primarily by improving specificity. The best-performing model—combining MRI, age, PSA, and PSAD with cross-attention—achieved a mean AUC of $$0.765\!\pm \!0.006$$ on the retrospective held-out test set and $$0.741\!\pm \!0.006$$ on the independent temporal validation cohort. Post hoc subgroup analyses suggested broadly stable rank-order discrimination (AUC) across most strata, though fixed-threshold sensitivity and specificity varied substantially, while Grad-CAM maps offered a qualitative indication of more frequent overlap with suspicious regions under cross-attention. These findings support PSAD-informed multimodal patient-level models as a viable tool for refined csPCa risk stratification on biparametric MRI, with potential to reduce false-positive classification of ISUP grade group 1 disease as clinically significant.
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: Dimitriadis A, Kalliatakis G, Osuala R, Kessler D, Diaz O, Mazzetti S, et al. Cross-attention fusion of biparametric MRI sequences with demographic and clinical variables for patient-level classification of clinically significant prostate cancer. Sci Rep. 2026 Oct 8 [Epub ahead of print]. doi:10.1038/s41598-026-71998-x.Checked: Abstract only - Abstract
Importance: The Decipher Prostate Genomic Classifier (GC) and ArteraAI Multimodal Artificial Intelligence (MMAI) platform are widely used prognostic tools for localized prostate cancer (PCa), yet no direct comparison has been performed within the same patient cohort.
Methods: We sought to evaluate the concordance and prognostic value of GC and MMAI across multi-institutional cohorts totaling 688 patients with localized and oligometastatic PCa. These included populations enriched for African American and East Asian patients. GC and MMAI scores were stratified into risk groups based on specimen type (biopsy or radical prostatectomy (RP)). The primary endpoint was distant metastasis-free survival (DMFS), measured from diagnosis to last follow-up or event.
Results: GC and MMAI scores were significantly correlated across multiple cohorts. In Moffitt RP, biopsy, and NCCS cohorts, both MMAI and GC were prognostic alone and after adjusting for clinical variables. Multivariable analysis including both biomarkers and clinical variables showed that MMAI was significant even accounting for GC in Moffitt RP, and borderline in Moffitt biopsy. Analysis of concordant/discordant cases showed that patients who were high-risk for both GC and MMAI did qualitatively worse. In pathway analysis, concordance with GC and MMAI was high for Moffitt RP and oligometastatic patients. Across both biopsy cohorts, while most biological pathways were concordant, there were consistent discordant signaling, metabolic, and DNA repair pathways.
Conclusions: In the first direct patient-level comparison of GC and MMAI in PCa, these biomarkers were found to demonstrate moderate correlation, and both biomarkers demonstrated prognostic value across diverse populations and disease states.
Journal IF-equivalent: 2.2 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Hong BH, Olabumuyi AA, Zhao SG, Trivedi P, Putney R, Katende E, et al. Multi-institutional Patient-level Comparison of Decipher Genomic Classifier and Artera Multimodal Artificial Intelligence (MMAI) in Prostate Cancer. Clinical Cancer Research. 2026 Oct 8 [Epub ahead of print]. doi:10.1158/1078-0432.ccr-26-2269.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
No new qualifying paper for this issue.
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
Objective: To construct various radiomics models based on CT venous-phase images for the quantitative assessment of gastric cancer (GC) invasion depth into the gastric wall, and to compare the diagnostic performance of different machine-learning models in distinguishing early (T1–T2 stage) from advanced (T3–T4 stage) GC, aiming to identify a robust and interpretable model and evaluate its potential for clinical application.
Methods: In this retrospective study, 223 pathologically confirmed GC patients (66 early-stage, 157 advanced-stage) treated between January 2022 and May 2025 were enrolled. Patients were allocated into a training set ( n = 156) and an internal hold-out test set ( n = 67) through stratified random sampling at a 7:3 ratio. All patients underwent enhanced CT within one week prior to surgery. Venous-phase images were selected, and three-dimensional regions of interest (ROIs) encompassing the tumor were manually delineated using 3D Slicer for radiomics feature extraction. To rigorously exclude data leakage, all preprocessing steps—including feature standardization, LASSO feature selection, hyperparameter tuning, and threshold determination—were performed exclusively within the training set using a pipeline-based approach with 5-fold cross-validation. Feature selection stability was further assessed using 50 iterations of repeated stratified cross-validation. Four machine learning models were constructed: Logistic Regression (LR), Random Forest (RF), XGBoost (XGB), and Support Vector Machine (SVM). Performance was evaluated using ROC curves, PR curves, confusion matrices, and decision curve analysis (DCA). SHAP analysis was applied to the LR model for interpretability.
Results: A total of 107 radiomics features were extracted, from which 23 key features were selected by LASSO regression. Feature selection stability analysis revealed that 2 features were consistently selected across all 50 iterations (100% frequency), 1 feature was retained in ≥ 98% iterations; overall 6 features exhibited selection frequency ≥ 80%, and 8 features achieved frequency ≥ 50%, while the remaining features showed considerable selection variability. In the internal hold-out test set, the LR model achieved the AUC of 0.926 (95% CI: 0.860–0.978), followed by SVM (AUC = 0.915, 95% CI: 0.848–0.973), RF (AUC = 0.893, 95% CI: 0.814–0.960), and XGBoost (AUC = 0.889, 95% CI: 0.806–0.956). DeLong’s test revealed no statistically significant differences in AUC among the four models (all P > 0.05); the marginal difference between LR and RF (AUC difference = 0.033, P = 0.051) only represented a weak numerical gap without statistical superiority of LR. The LR model demonstrated balanced performance with accuracy 0.836, sensitivity 0.830, specificity 0.850, PPV 0.929, NPV 0.680, F1-score 0.876, and average precision (AP) 0.972; notably, LR yielded the lowest false-negative rate (17.0%), which minimizes the clinical risk of undertreating advanced GC. Hosmer-Lemeshow test revealed good calibration for the Random Forest ( P = 0.586), XGBoost ( P = 0.081), and SVM ( P = 0.213) models, whereas the Logistic Regression model showed evidence of miscalibration ( P < 0.001). Despite this calibration limitation, the LR model’s excellent discriminative performance (AUC = 0.926) supports its clinical utility as a binary classifier. DCA indicated that all four models provided clinical utility, with the LR model offering favorable net benefit across most threshold probabilities. SHAP analysis confirmed that the contribution directions of selected features such as LeastAxisLength and SurfaceVolumeRatio were consistent with pathological interpretations.
Conclusion: All four machine learning models constructed based on CT venous-phase images showed comparable discriminative power, with no statistically significant inter-model AUC differences detected via DeLong test (all P > 0.05). Rather than possessing statistically superior predictive efficacy, the LR model had higher clinical translation potential due to its excellent interpretability, balanced classification metrics and minimal false-negative risk. The LR model, with transparent coefficient structure and good diagnostic efficacy, can serve as a quantitative auxiliary tool for preoperative staging and treatment decision-making, providing an objective basis for precise GC diagnosis and treatment. Further multi-center external prospective validation is mandatory before formal clinical deployment.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Zhang H, Liu Q, Lian W, Huang R, Zhao X, Li L, et al. CT venous-phase radiomics for preoperative differentiation of early and advanced gastric cancer: construction, comparison, and validation of machine-learning models. Cancer Imaging. 2026 Oct 8 [Epub ahead of print]. doi:10.1186/s40644-026-01132-7.Checked: Abstract only
Liver cancer: treatment, trials and AI
- Abstract
Background/Objectives: Treatment allocation for hepatocellular carcinoma (HCC) is a highly complex task that requires multidisciplinary tumor board (MDT) input; however, its accessibility and reliability can vary across the healthcare system. Large language models (LLMs) have emerged as potential clinical decision-making tools to aid MDTs. Our aim was to compare different LLM-generated treatment recommendations for HCC cases with MDT decisions.
Methods: We retrospectively analyzed 100 HCC cases discussed during MDT meetings in a tertiary-care hospital in Cluj-Napoca, Romania. Identical prompts and structured clinical information were offered to four different LLMs (ChatGPT-5, a customized Tumor Board ChatGPT, Gemini 2.5 Flash, and Gemini 2.5 Pro), each of which was required to provide treatment recommendations. Concordance with the MDT decisions was assessed across the first, second, and third treatment options. Inter-model differences were evaluated using Cochran’s Q and Holm-adjusted exact McNemar tests. Chance-corrected agreement was assessed using Cohen’s kappa (κ), and generalized estimating equations evaluated associations between concordance and clinical complexity.
Results: First-recommendation concordance was 81% (95% CI 72.2–87.5) for GPT-5, 80% (71.1–86.7) for Gemini 2.5 Pro, 78% (68.9–85.0) for TumorBoard ChatGPT, and 63% (53.2–71.8) for Gemini 2.5 Flash; cumulative top-3 concordance reached 95%, 92%, 95%, and 86%, respectively. GPT-5 (κ = 0.754) and Gemini 2.5 Pro (κ = 0.743) showed substantial chance-corrected agreement. Gemini 2.5 Flash showed significantly lower concordance, including after adjustment for BCLC stage, Child–Pugh class, tumor burden, and treatment category (p = 0.006), while these clinical complexity variables were not significantly associated with concordance.
Conclusions: LLMs showed moderate-to-substantial agreement with MDT decisions, with meaningful inter-model differences, for a complex disease, such as HCC. Our study highlights that, while the need for human oversight is essential, LLMs could serve as supportive tools for expert guidance in some clinical settings and underscore AI’s potential in enhancing liver cancer care.
Journal IF-equivalent: 2.1 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Grapa C, Mocan T, Leucuta DC, Mocan LP, Craciun R, Stefanescu H, et al. Concordance Between Large Language Models and Multidisciplinary Tumor Board Recommendations in Treatment Allocation for Hepatocellular Carcinoma. Livers. 2026 Oct 8;6(5):104. doi:10.3390/livers6050104.Checked: Abstract only - Abstract
INTRODUCTION: This study assessed whether Gd-DTPA-enhanced MRI could predict the immunoscore in HCC noninvasively before therapy, without the need for tissue sampling. METHODS: Retrospectively, surgically treated HCC patients who had a preoperative Gd-DTPA-enhanced MRI exam were enrolled. Eligible patients were randomly allocated to a training set and a validation set following a 3:1 randomization scheme. Immunohistochemistry was performed to quantify CD3-positive and CD8-positive cell densities; patients were stratified into high and low immunoscore groups using the median cutoff. Volumes of interest encompassing hepatic lesions, including intratumoral and peritumoral 10-mm margins, were manually delineated on multiparametric MRI sequences for radiomics feature extraction. Clinical and pathological data were retrieved from electronic medical records. A Support Vector Machine (SVM) was used to develop three predictive models: (1) Intratumoral Radiomics Model (IRM), (2) combined intra- and peritumoral radiomics model (CRM), and (3) Clinical Combined Radiomics Model (CCRM). Model performance was compared using the DeLong test and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, and Decision Curve Analysis (DCA). RESULTS: Of 111 eligible patients, 83 were assigned to the training cohort and 28 to the validation cohort. Baseline characteristics were balanced between cohorts. The CCRM demonstrated the highest AUC values in both cohorts (training: 0.947; validation: 0.913). Compared with IRM, CCRM showed significantly better performance in the validation cohort (p = 0.016) but not in the training cohort (p = 0.084). In the validation cohort, CCRM did not significantly outperform CRM (p = 0.55), whereas a significant difference was observed in the training cohort (p = 0.036). Calibration curves demonstrated good agreement. DCA indicated that the CCRM and the CRM achieved similar overall net benefits, which were higher than those of the IRM across threshold probabilities of 46% to 98%. DISCUSSION: This radiomics approach suggests the feasibility of using routine Gd-DTPA-enhanced MRI for preoperative immunoscore prediction in HCC, providing preliminary evidence for tumor immune characterization. CONCLUSION: A radiomics model integrating intra- and peritumoral MRI features enables noninvasive preoperative immunoscore prediction in HCC, which may provide preliminary support for individualized immunotherapy decision-making, although external validation is warranted.
Journal IF-equivalent: 1.3 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Wang J, Jiang Y, Li Z, Xu D, Teng H, Meng H, et al. Radiomics Model Based on Gd-DTPA-enhanced Multiparametric MRI Supports Noninvasive Pretreatment Prediction of Immunoscore in Hepatocellular Carcinoma. CMIR. 2026 Sep 30;22. doi:10.2174/0115734056504350260925115431.Checked: Abstract only
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.