Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer

Advances in the use of Radiomics and Pathomics for predicting the efficacy of neoadjuvant therapy in tumors

Neoadjuvant therapy is widely used for treating malignant tumors, but its efficacy varies among patients. Currently, tools or biomarkers for early and accurate evaluation of the efficacy of neoadjuvant therapy are lacking. The advent of radiomics and pathomics offers new avenues for refining neoadjuvant therapy strategies and could provide high-performance predictive tools. The integration of multi-omics represents an emerging area of research. The introduction of radiopathomics offers innovative approaches to studying the efficacy of neoadjuvant therapy. This article reviews the current developments in multi-omics integration, the advances in the use of radiopathomics to predict the efficacy of neoadjuvant therapy, and the challenges faced by ongoing research.

Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics

Objective

This study aimed to develop a comprehensive nomogram model by integrating clinical pathological and full-field digital mammography (FFDM) radiomic features to predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients, thereby providing personalized treatment recommendations.

Methods

A retrospective analysis was conducted on the clinical and imaging data of 227 breast cancer patients from 2016 to 2024 at the Second Affiliated Hospital of Harbin Medical University. The patients were divided into a training set (n = 159) and a test set (n = 68) with a 7:3 ratio. The region of interest (ROI) was manually segmented on FFDM images, and features were extracted and gradually selected. The rad-score was calculated for each patient. Five machine learning classifiers were used to build radiomics models, and the optimal model was selected. Univariate and multivariate regression analyses were performed to identify independent risk factors for predicting the efficacy of NAC in breast cancer patients. A nomogram prediction model was further developed by combining the independent risk factors and rad-score, and probability-based stratification was applied. An independent cohort was collected from an external hospital to evaluate the performance of the model.

Results

The radiomics model based on support vector machine (SVM) demonstrated the best predictive performance. FFDM tumor density and HER-2 status were identified as independent risk factors for achieving pathologic complete response (PCR) after NAC (p < 0.05). The nomogram prediction model, developed by combining the independent risk factors and rad-score, outperformed other models, with areas under the curve (AUC) of 0.91 and 0.85 for the training and test sets, respectively. Based on the optimal cutoff points of 103.42 from the nomogram model, patients were classified into high-probability and low-probability groups. When the nomogram model was applied to an independent cohort of 47 patients, only four patients had incorrect diagnoses. The nomogram model demonstrated stable and accurate predictive performance.

Conclusion

The nomogram prediction model, developed by integrating clinical pathological and radiomic features, demonstrated significant performance in predicting the efficacy of NAC in breast cancer, providing valuable reference for clinical personalized prediction planning.

MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific

Breast magnetic resonance imaging (MRI) is the most sensitive imaging method for diagnosing breast cancer and assessing treatment response. Artificial intelligence (AI) and radiomics offer new opportunities to identify patterns in imaging data, supporting personalized post-neoadjuvant surgical decisions. This paper reviewed breast MRI-based AI models for predicting outcomes after neoadjuvant therapy, with a focus on evidence from the Western Pacific region, to evaluate the quality of existing models, discuss their inherent limitations, and outline potential future directions. A literature search in MEDLINE, EMBASE, and Web of Science identified 51 relevant studies in the region, with the majority conducted in China, followed by South Korea and Japan. Most studies focused on predicting pathologic complete response (pCR), with a median sample size of 152 and largely retrospective single-center designs. Model performance was commonly assessed using validation sets, with pooled sensitivity and specificity for pCR prediction showing promising results. Models incorporating multitemporal MRI features were associated with improved accuracy. While MRI-based AI models show potential for guiding surgical planning, improved methodological quality and algorithmic explainability are needed to facilitate clinical translation.

Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature

OBJECTIVE

This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

This study involved 126 HCC patients who underwent hepatectomy and were followed for more than 5 years. Radiomic features were extracted from arterial-phase (AP) and portal venous-phase (PVP) MRI scans, whereas pathomic features were obtained from whole-slide images (WSIs) of the HCC patients. Using LASSO Cox regression, both radiomics and pathomics signatures were established. A combined radiopathomics nomogram for predicting OS was constructed and validated. The correlation between the radiopathomics nomogram and OS prediction was evaluated, demonstrating its potential clinical utility in prognosis assessment.

RESULTS

We selected four radiomic features from the AP and PVP MRI scans to construct a signature, achieving a concordance index (C-index) of 0.739 in the training cohort and 0.724 in the validation cohort; these results indicate favourable 5-year OS prediction. Similarly, from 1,141 pathomics features extracted from WSIs, 15 were chosen for a pathomics signature, which had C-indexes of 0.821 and 0.808 in the training and validation cohorts, respectively. The most robust performance was delivered by a radiopathomics nomogram, with C-index values of 0.840 in the training cohort and 0.875 in the validation cohort. Decision curve analysis (DCA) confirmed the highest net benefit achievable by the combined radiopathomics nomogram.

CONCLUSION

Our findings indicate that the radiopathomics nomogram can serve as a predictive marker for hepatectomy prognosis in HCC patients and has the potential to enhance personalized therapeutic approaches.

Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study

OBJECTIVE

This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM).

METHODS

A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics features and 254 pathomics features were initially extracted. Feature selection involved intra-class correlation coefficient evaluation, Mann-Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. The PSO-SVM model was constructed and validated using 10-fold cross-validation on the training set and further evaluated using an external validation set. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, and decision curve analysis.

RESULTS

Eight significant predictive features (five radiomics and three pathomics) were identified. The PSO-SVM radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the PSO-SVM radiopathomics model were 0.917 (95% CI: 0.887-0.948) in internal validation and 0.814 (95% CI: 0.742-0.887) in external validation. Calibration curves demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis showed that the PSO-SVM radiopathomics model provided higher clinical net benefit over a wider range of risk thresholds compared to other models.

CONCLUSION

The PSO-SVM radiopathomics model effectively integrates radiomics and pathomics features, offering enhanced predictive accuracy and clinical utility for assessing NACT efficacy in NPC. The multi-center approach and robust validation underscore its potential for personalized treatment planning, supporting improved clinical decision-making for NPC patients.