ArticleNPJ precision oncology2025
Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Interpretable prediction of occult lymph node metastasis in pancreatic ductal adenocarcinoma using a model fusing habitat radiomics and deep learning.Abdominal radiology (New York) · 2026Article
- Multichannel deep learning-based MRI model for predicting breast cancer axillary lymph node invasion: a comparative study to Node-RADS.Quantitative imaging in medicine and surgery · 2026Article
- A neutrophil-to-lymphocyte ratio-centered machine learning model for predicting immunotherapy response in esophageal squamous cell carcinoma patients.Journal of thoracic disease · 2026Article
- CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.Journal of translational medicine · 2026Article
- A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.NPJ Parkinson's disease · 2026Article
- Multimodal radiomics incorporating intratumoral heterogeneity for prognostic assessment of metastatic outcomes in invasive breast cancer.Breast cancer research : BCR · 2026Article
- Macro Habitat-Based T2-Weighted MRI Radiomics and Deep Learning Fusion for Predicting Treatment Response and Prognosis After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.Cancer medicine · 2026Article
- Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.NPJ precision oncology · 2026Article
- Unlocking the potential of radiomics in predicting the response of neoadjuvant immunochemotherapy for operable locally advanced esophageal squamous cell carcinoma: a narrative review.Journal of thoracic disease · 2026Review
- A CT-based multi-scale fusion model with SHAP interpretation for preoperative differentiation between lung adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach.Frontiers in oncology · 2026Article
- Habitat-radiomics combining multichannel 2.5D deep learning for differentiating adrenal adenomas from metastases using automatic segmentation: a multicenter study.Frontiers in endocrinology · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
This study aimed to develop and compare fusion models combining sub-regional radiomics with multichannel 2D and 3D DL to predict pCR in patients with LA-ESCC undergoing NACI. A total of 271 patients from three hospitals were divided into training, internal validation, and external validation cohorts. Tumor sub-regions were identified using K-means clustering based on radiomic features, and predictive features were extracted using PyRadiomics. Among all models, the DLRad1 model (radiomics + 2D DL) demonstrated the highest performance, with an AUC ranging from 0.793 to 0.910 across cohorts. Sub-region 1 features alone achieved an AUC of 0.823, while DLRad2 (radiomics + 3D DL) and other single-modality models showed lower AUCs (0.701-0.906). Spearman correlation analysis confirmed low redundancy among selected features. These findings support DLRad1 as a promising non-invasive tool to identify LA-ESCC patients most likely to benefit from NACI, potentially aiding personalized treatment decisions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.