ArticleFrontiers in oncology2022
Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.
- Pertuzumab and Pathological Complete Response in Early HER2+ Breast Cancer: A Systematic Review and Meta-analysis of Real-World Studies.Asian Pacific journal of cancer prevention : APJCP · 2026Pooled it
- Pooled it
- Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study.BMC infectious diseases · 2025Article
- Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound.Journal of translational medicine · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods. Methods: A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion - post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram's performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. Results: The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002-1.024; Conclusion: Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient's clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.
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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.