AIMC Topic: 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 Aug 23, 2022
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...
Humans Machine Learning Prognosis Risk Factors Colorectal Neoplasms Chemotherapy, Adjuvant
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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 Aug 11, 2022
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...
Colorectal Neoplasms Deep Learning Humans Decision Support Systems, Clinical Prognosis Neoplasm Staging Chemotherapy, Adjuvant
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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 Mar 24, 2022
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...
Colorectal Neoplasms Biomarkers, Tumor Deep Learning Humans Antineoplastic Combined Chemotherapy Protocols Neoplasm Staging Fluorouracil Artificial Intelligence Chemotherapy, Adjuvant Prospective Studies Prognosis Retrospective Studies
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Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Nature communications Feb 10, 2022
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-base...
Gene Expression Profiling B7-H1 Antigen Gene Expression Regulation, Neoplastic Chemotherapy, Adjuvant Machine Learning RNA, Long Noncoding Neoplasm Recurrence, Local Humans CD8 Antigens Risk Factors Fluorouracil Colorectal Neoplasms Biomarkers, Tumor
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Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

Scientific reports Sep 27, 2021
High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist ...
Aged Adult Middle Aged Treatment Outcome Neural Networks, Computer Artificial Intelligence Retrospective Studies Female Platinum Compounds Fallopian Tube Neoplasms Ovarian Neoplasms Peritoneal Neoplasms Chemotherapy, Adjuvant Cystadenocarcinoma, Serous Humans
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Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

Scientific reports Jul 19, 2021
For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is h...
Gene Regulatory Networks Gene Expression Regulation, Neoplastic Predictive Value of Tests Mastectomy Microarray Analysis Chemotherapy, Adjuvant Neoplasm Recurrence, Local Survival Analysis Breast Neoplasms Biomarkers, Tumor Gene Expression Profiling Transcriptome Artificial Intelligence Machine Learning Prognosis Female Humans Neural Networks, Computer
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Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

European journal of cancer (Oxford, England : 1990) Feb 24, 2021
PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatme...
Neoplasm Invasiveness Predictive Value of Tests Ultrasonography, Mammary Mastectomy Deep Learning Retrospective Studies Nomograms Chemotherapy, Adjuvant Clinical Decision-Making Treatment Outcome Image Interpretation, Computer-Assisted Reproducibility of Results Humans Female Neoadjuvant Therapy Observer Variation Adult Breast Neoplasms Middle Aged
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Comparing Laparotomy with Robot-assisted Interval Debulking Surgery for Patients with Advanced Epithelial Ovarian Cancer Receiving Neoadjuvant Chemotherapy.

Journal of minimally invasive gynecology Nov 26, 2020
STUDY OBJECTIVE: Compare survival of patients with advanced epithelial ovarian cancer (EOC) undergoing interval debulking surgery (IDS) with either robot-assisted (R-IDS) or open (O-IDS) approach. Second, we assessed the impact of adjuvant and neoadj...
Robotic Surgical Procedures Carcinoma, Ovarian Epithelial Cytoreduction Surgical Procedures Laparotomy Chemotherapy, Adjuvant Neoadjuvant Therapy Ovarian Neoplasms Robotics Neoplasm Staging Humans Retrospective Studies Female Aged
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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery Jun 16, 2020
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...
Breast Neoplasms Middle Aged Antineoplastic Agents Humans Diagnosis, Computer-Assisted Neoadjuvant Therapy Neural Networks, Computer Contrast Media Retrospective Studies Magnetic Resonance Imaging Female Area Under Curve Adult Deep Learning Chemotherapy, Adjuvant ROC Curve Image Processing, Computer-Assisted Treatment Outcome
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Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

Thoracic cancer Jan 16, 2020
BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer.
Antineoplastic Combined Chemotherapy Protocols Breast Neoplasms Carcinoma, Papillary Middle Aged Carcinoma, Lobular Neoadjuvant Therapy Carcinoma, Ductal, Breast Humans Deep Learning Follow-Up Studies Chemotherapy, Adjuvant Prognosis ROC Curve Neoplasm Invasiveness Retrospective Studies Female
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