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Chemotherapy, Adjuvant

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mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.

Frontiers in immunology
Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

Journal of cancer research and clinical oncology
PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has be...

Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence.

Techniques in coloproctology
BACKGROUND: Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using ...

A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study.

The Lancet. Oncology
BACKGROUND: The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after p...

Biology-guided deep learning predicts prognosis and cancer immunotherapy response.

Nature communications
Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatmen...

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

Cell reports. Medicine
The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining...

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.

Academic radiology
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning rad...

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

Breast cancer research and treatment
PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients ...

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

Gynecologic oncology
OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.