AI and Cancer Research ── Breast ── 2026-10-18
Breast cancer: treatment, trials and AI
- Abstract
Distant metastasis is a major cause of breast cancer mortality. Early prediction may support risk-adapted surveillance and treatment planning. We evaluated imaging-based machine learning (ML) and deep learning (DL) models for predicting future distant metastasis in patients without baseline metastatic disease. PubMed, Scopus, Web of Science, and supplementary Google Scholar searches were performed through June 2025. Eligible studies evaluated imaging-based AI prediction models and reported quantitative performance. The primary outcome was the area under the receiver operating characteristic curve (AUC). Risk of bias was assessed using PROBAST + AI. Random-effects meta-analysis, exploratory subgroup analyses, and univariable meta-regression were performed. Nine studies contributed 43 model evaluations. The pooled model-level AUC was 0.80 (95% CI: 0.77–0.84). A secondary analysis restricted to the best-performing model from each study yielded an AUC of 0.89 (95% CI: 0.83–0.95). CT-based models were associated with higher AUCs than MRI-based models (0.83 vs. 0.71), and models incorporating clinicopathological features had higher AUCs than imaging-only models (0.91 vs. 0.74). Exploratory meta-regression identified associations with imaging modality, follow-up duration, and inclusion of clinicopathological features. Heterogeneity was substantial, and five of nine studies had high risk of bias. Imaging-based ML and DL models show potential for predicting future distant metastasis. In the adjuvant setting, externally validated models may support risk stratification and individualized surveillance, but current evidence is insufficient to justify surveillance or treatment changes based solely on AI predictions. Prospective external validation and transparent reporting are needed before clinical implementation.
Journal IF-equivalent: 2.5 (OpenAlex 2-year mean citedness, value as of 2026-10-17, retrieved 2026-10-18; not the official Clarivate IF)Reference: Dehghani SS, Zakeri A, Zahedinejad S, Nafisi M, Habibi S, Vardanjani HM. Performance of machine learning (ML) and deep learning (DL) algorithms in prediction of breast cancer distant metastasis via imaging: a systematic review and meta‑analysis. Langenbecks Arch Surg. 2026 Oct 16 [Epub ahead of print]. doi:10.1007/s00423-026-04344-4.Checked: Abstract only - Abstract
Objective: To evaluate the incremental predictive value of pretreatment tri-modal ultrasound deep learning integrating two-dimensional ultrasound (2DUS), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) for predicting breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC), and to assess performance stability across independent centers.
Methods: This retrospective multicenter study included 1089 patients with primary invasive breast cancer. Center 1 provided the training cohort (n = 572) and internal validation cohort (n = 246), whereas centers 2 (n = 160) and 3 (n = 111) served as independent external validation cohorts. Using pretreatment 2DUS, CDFI, and CEUS within a standardized intratumoral and 10-mm peritumoral framework, single-modality, all three dual-modality combinations, and tri-modal models were developed. The best-performing dual-modality model was identified and locked during training as the primary imaging comparator. A Tri-modal + Clinical model further integrated pretreatment clinical and biomarker variables. Performance was assessed using AUC, Brier score, calibration, decision curve analysis, and ablation experiments.
Results: Overall, 383 patients (35.2%) achieved bpCR. CEUS was the strongest single modality, and 2DUS + CEUS was the best-performing dual-modality model. Compared with 2DUS + CEUS, the tri-modal model increased the AUC from 0.895 to 0.919 in the combined external cohort (ΔAUC, +0.023; 95% CI, +0.010 to +0.038; p = 0.001), with corresponding improvements in Brier score (0.124 to 0.105) and F1 score (0.747 to 0.786). The AUC point estimate increased in both external centers, although the 95% CI for the incremental AUC included zero in external validation cohort 2. Despite modest standalone discrimination, CDFI provided measurable incremental information when added to 2DUS + CEUS. Further integration of pretreatment clinical and biomarker variables increased the combined external AUC to 0.935. Reduced-input retraining showed performance decreases after omission of any modality, with the largest reduction after CEUS omission.
Conclusions: Pretreatment tri-modal ultrasound deep learning provided incremental predictive value for bpCR beyond the best dual-modality model, with a consistent direction of benefit across two independent external centers. Clinical and biomarker integration provided additional predictive information, supporting further evaluation of this multimodal framework for individualized pretreatment bpCR prediction.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Wei C, Pan T, Li T, Zheng C, Duan Y, Nie F. Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study. Cancers. 2026 Oct 15;18(20):3326. doi:10.3390/cancers18203326.Checked: Abstract only - Abstract
Background: Ductal carcinoma in situ (DCIS) may be upstaged to invasive carcinoma after surgical excision. Predicting upstaging before surgery may help guide treatment planning. Artificial intelligence (AI)-based imaging has been investigated for this purpose, but its overall performance remains unclear.
Objective: To evaluate the diagnostic accuracy of AI-based systems for predicting DCIS upstaging to invasive carcinoma. Materials and Methods: A systematic review and meta-analysis was conducted following PRISMA 2020 and registered in PROSPERO (CRD420261516994). PubMed, Scopus, EBSCOhost, ProQuest, and Web of Science were searched from inception to September 2026. Studies evaluating AI-based imaging models for DCIS upstaging were included. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. Risk of bias was assessed using QUADAS-2.
Results: Six studies involving 701 participants were included. Pooled sensitivity was 0.77 (95% CI, 0.57–0.89) and specificity was 0.78 (95% CI, 0.64–0.87), with substantial heterogeneity (I² = 84.0% and 90.6%, respectively). The pooled log diagnostic odds ratio (DOR) was 2.15 (95% CI, 1.64–2.65). The SROC analysis showed moderate overall discriminative performance. No significant differences were observed across imaging modalities, while AI model type significantly affected specificity but not sensitivity. Deeks’ funnel plot suggested possible publication bias (p = 0.046).
Conclusion: AI-based imaging showed moderate accuracy for predicting DCIS upstaging. However, substantial heterogeneity and methodological limitations limit confidence in these findings. Further prospective studies with external validation are needed.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Qelmi TS, Raditya AF, Nandini KA, Kencono CD. DIAGNOSTIC ACCURACY OF ARTIFICIAL INTELLIGENCE IN THE UPSTAGING PREDICTION OF DUCTAL CARCINOMA IN SITU: A SYSTEMATIC REVIEW AND META-ANALYSIS. World J Adv Res Rev. 2026 Oct 31;32(1):802-11. doi:10.30574/wjarr.2026.32.1.2580.Checked: Abstract only