ArticleBMC medical imaging2024
Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 5 citations in OpenAlex.
- Incremental Value of Synthetic MRI Radiomics in Predicting Neoadjuvant Response in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.Radiology. Imaging cancer · 2026Article
- Development and internal validation of a nomogram for predicting neoadjuvant therapy efficacy in breast cancer based on the HALP score.Translational cancer research · 2026Article
- Longitudinal Breast MRI for Early Treatment-Response Modeling: A Comparative Study of Handcrafted Radiomics and Frozen Deep Image Embeddings for pCR Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- APASA: adaptive selection of informative peritumoral regions for improved automated cancer lesion analysis.Scientific reports · 2026Article
- A multimodal deep learning radiomics nomogram integrating clinicopathological features for predicting pathological complete response after neoadjuvant therapy in triple-negative breast cancer: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI.BMC cancer · 2025Article
- Article
- MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis.Journal of clinical medicine · 2024Article
- Tumor size, HER-2 status, CA125, CEA, SII, and PNI: key predictors of pathological complete response in LABC patients.American journal of cancer research · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundNon-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT.
methodsThe clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors.
resultsAmong single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05).
conclusionBPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.
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