ArticleBreast cancer research and treatment2022
Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer.
Article in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Application of MR Cytometry in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparative Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer.Translational cancer research · 2025Article
- Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.BMC cancer · 2025Article
- Advancing breast cancer research through real-world data from the Singapore Joint Breast Cancer Registry.ESMO real world data and digital oncology · 2025Article
- Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting Very Early-Stage Breast Cancer in BI-RADS 3 Lesions of Large Population with Deep Learning.Journal of imaging · 2025Article
- Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer.Journal of translational medicine · 2025Article
- Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases.Journal of the anus, rectum and colon · 2025Article
- Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models.Frontiers in oncology · 2025Article
- Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer.Insights into imaging · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.Quantitative imaging in medicine and surgery · 2023Article
- Predictive value ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- Deep convolution neural network for screening carotid calcification in dental panoramic radiographs.PLOS digital health · 2023Article
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- Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis.Frontiers in oncology · 2022Article
- GRACE-ViT: Grouped Recalibration With Adaptive Contextual Emphasis for Breast Cancer Neoadjuvant Chemotherapy Response Prediction.Technology in cancer research & treatmentArticle
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Authors and funding
25 authors.
Funding
Abstract
backgroundNeoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC.
methodsThe CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model.
resultsThe best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855).
conclusionsRadiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.
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