AIMC Topic: Neoadjuvant Therapy

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Translational and real-world evidence of trastuzumab biosimilar CT-P6 plus pertuzumab in neoadjuvant HER2-positive early breast cancer.

Breast cancer research and treatment
BACKGROUND: Data on neoadjuvant treatment with trastuzumab biosimilars, particularly CT-P6, in combination with pertuzumab, are limited. This study evaluates the efficacy, tolerability, and immunogenicity of CT-P6 plus pertuzumab and chemotherapy, in...

Interpretable multimodal radiopathomics model predicting pathological complete response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Journal for immunotherapy of cancer
BACKGROUND: Accurate preoperative prediction of pathological complete response (pCR) following neoadjuvant chemoimmunotherapy (nCIT) could help individualize treatment for patients with esophageal squamous cell carcinoma (ESCC). This study aimed to d...

Machine learning combined with body composition predicts surgical difficulty in mid-low rectal cancer surgery.

Annals of medicine
BACKGROUND: This study sought to identify critical body composition characteristics associated with surgical difficulty in Laparoscopic Total Mesorectal Excision (LaTME) and to develop and validate an interpretable machine learning model using body c...

Ensemble model for neoadjuvant chemotherapy response prediction and treatment sensitivity in TNBC based on DNA replication stress signatures.

Scientific reports
Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer. Although neoadjuvant chemotherapy (NACT) has some effectiveness in TNBC, a portion of patients still do not benefit from them. The critical role of DNA replication ...

Axillary lymph node dissection offers no survival benefit in breast cancer patients with sentinel lymph node micrometastases after neoadjuvant therapy.

Clinical and experimental medicine
The role of axillary lymph node dissection (ALND) in breast cancer patients with sentinel lymph node (SLN) micrometastases, particularly after neoadjuvant therapy, remains debated. The present study aimed to assess whether adding ALND provides a surv...

Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.

International journal of colorectal disease
OBJECTIVE: In this study, we constructed a new pCR predictor based on dynamic tumor marker changes before and after NCRT, the dynamic tumor marker score (DTMS), and combined it with other clinicopathological features to build a machine-learning model...

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
BACKGROUND: This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy...

Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.

Journal for immunotherapy of cancer
Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to...

Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.

Annals of medicine
BACKGROUND: Most models of neoadjuvant chemotherapy (NACT) for breast cancer (BC) suffer from insufficient data and lack interpretability. Additionally, there is a notable absence of reports from China in this field. This study is also the first to i...

Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.

BMC medical imaging
This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (N...