ArticleBreast cancer research and treatment2021
Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer.
Article in Breast cancer research and treatment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07426653 (Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer), which is not on this map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Who cites it
17 citing papers in PubMed.
- Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.BMC cancer · 2026Article
- Article
- Prediction of pathological complete response after neoadjuvant therapy in breast cancer using pretreatment core biopsy pathology reports: a pathology-report-driven model with exploratory clinical-laboratory fusion.Frontiers in oncology · 2026Article
- Correlation Study Between Neoadjuvant Chemotherapy Response and Long-Term Prognosis in Breast Cancer Based on Deep Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
- Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics.Cancer medicine · 2025Article
- Article
- 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
- Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy.NPJ breast cancer · 2025Article
- Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers.Breast cancer research : BCR · 2025Article
- Recent advances in the bench-to-bedside translation of cancer nanomedicines.Acta pharmaceutica Sinica. B · 2025Review
- Markers of Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer: New in Molecular Oncology.Asian Pacific journal of cancer prevention : APJCP · 2024Review
- Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach.Breast cancer research : BCR · 2024Article
- Article
- Review
- Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.Journal of breast cancer · 2023Article
- Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network.BMC medical informatics and decision making · 2023Article
- Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop a machine learning (ML) based model to accurately predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) using pretreatment clinical and pathological characteristics of electronic medical record (EMR) data in breast cancer (BC).
methodsThe EMR data from patients diagnosed with early and locally advanced BC and who received NAC followed by curative surgery were reviewed. A total of 16 clinical and pathological characteristics was selected to develop ML model. We practiced six ML models using default settings for multivariate analysis with extracted variables.
resultsIn total, 2065 patients were included in this analysis. Overall, 30.6% (n = 632) of patients achieved pCR. Among six ML models, the LightGBM had the highest area under the curve (AUC) for pCR prediction. After hyper-parameter tuning with Bayesian optimization, AUC was 0.810. Performance of pCR prediction models in different histology-based subtypes was compared. The AUC was highest in HR+HER2- subgroup and lowest in HR-/HER2- subgroup (HR+/HER2- 0.841, HR+/HER2+ 0.716, HR-/HER2 0.753, HR-/HER2- 0.653).
conclusionsA ML based pCR prediction model using pre-treatment clinical and pathological characteristics provided useful information to predict pCR during NAC. This prediction model would help to determine treatment strategy in patients with BC planned NAC.
Indexed as
Identifiers
34224056What OpenQuestion holds
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.