AIMC Topic: Antineoplastic Combined Chemotherapy Protocols

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A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

Breast cancer research : BCR May 28, 2020
BACKGROUND: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and va...
Humans Machine Learning Prognosis Antineoplastic Combined Chemotherapy Protocols Retrospective Studies Neoadjuvant Therapy Female Follow-Up Studies Breast Neoplasms Magnetic Resonance Imaging Middle Aged Carcinoma, Ductal, Breast ROC Curve Carcinoma, Lobular
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Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 20, 2020
PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.
Docetaxel Humans Machine Learning Clinical Trials, Phase III as Topic Gefitinib Prognosis Antineoplastic Combined Chemotherapy Protocols Retrospective Studies Carcinoma, Non-Small-Cell Lung Randomized Controlled Trials as Topic Tomography, X-Ray Computed Clinical Trials, Phase II as Topic Female Nivolumab Survival Rate Male Disease Progression Lung Neoplasms
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Machine Learning for Cancer Drug Combination.

Clinical pharmacology and therapeutics Feb 11, 2020
When treating multiple complex diseases, such as cancer, polytherapy may demonstrate efficiency than monotherapy. However, due to the multiplicative relationship between the number of drugs and cell lines vs. the number of combinations, it is impract...
Drug Development Humans Machine Learning Forecasting Computer Simulation Antineoplastic Combined Chemotherapy Protocols
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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.
Neoplasm Invasiveness Carcinoma, Ductal, Breast Chemotherapy, Adjuvant Carcinoma, Papillary Carcinoma, Lobular Deep Learning ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Humans Prognosis Retrospective Studies Female Breast Neoplasms Middle Aged
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FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.

Cancer medicine Jan 1, 2020
Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5-FU, leucovorin and oxaliplatin) therapy is very important. We performed microarray meta-analysis to identify differentially expressed ...
Humans Gene Expression Regulation, Neoplastic Antineoplastic Combined Chemotherapy Protocols Neoplasm Recurrence, Local Fluorouracil Protein Kinases Datasets as Topic Machine Learning Prognosis Colorectal Neoplasms Biomarkers, Tumor Gene Expression Profiling Leucovorin Organoplatinum Compounds Cell Cycle Proteins Oligonucleotide Array Sequence Analysis Intracellular Signaling Peptides and Proteins Response Evaluation Criteria in Solid Tumors
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Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.

Cancer medicine Dec 20, 2019
BACKGROUND: To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.
Chemoradiotherapy Nasopharyngeal Neoplasms Endemic Diseases Nasopharynx Induction Chemotherapy Cisplatin Fluorouracil Progression-Free Survival Radiotherapy, Intensity-Modulated Kaplan-Meier Estimate Deep Learning China Image Processing, Computer-Assisted Antineoplastic Combined Chemotherapy Protocols Neoplasm Staging Humans Retrospective Studies Prognosis Female Adult Male Middle Aged Risk Assessment
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Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.

Gut Nov 28, 2019
OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.
Colonic Neoplasms Disease-Free Survival Image Interpretation, Computer-Assisted Prognosis Humans Antineoplastic Combined Chemotherapy Protocols Artificial Intelligence Adenocarcinoma Lymphocytes, Tumor-Infiltrating Neoplasm Invasiveness Neoplasm Staging
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Models, Genetic Tumor Microenvironment Microtubules Humans Brain Neoplasms Tumor Suppressor Protein p53 Chemoradiotherapy, Adjuvant Antineoplastic Combined Chemotherapy Protocols Machine Learning Immune Checkpoint Inhibitors Tumor Suppressor Proteins Clonal Evolution Glioblastoma Brain Computational Biology Mutation DNA Modification Methylases Precision Medicine Cancer Vaccines Genetic Heterogeneity DNA Mutational Analysis Prognosis DNA Repair Enzymes Drug Resistance, Neoplasm
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[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data].

Bulletin du cancer Oct 3, 2019
INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuation of treatment and is also a powerful prognostic factor. There are currently no reliable ways to eva...
Infant Adolescent Child, Preschool Child Antineoplastic Combined Chemotherapy Protocols Retrospective Studies Treatment Outcome Humans Female France Neoadjuvant Therapy Machine Learning Infant, Newborn Analysis of Variance Magnetic Resonance Imaging Young Adult Chemotherapy, Adjuvant Osteosarcoma Sensitivity and Specificity Bone Neoplasms Male Predictive Value of Tests
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A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy.

PloS one Oct 2, 2019
PURPOSE: Development of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.
Retrospective Studies Keratins, Hair-Specific Keratins, Type II Female Leucovorin Adult Fluorouracil Middle Aged Humans Survival Rate Antineoplastic Combined Chemotherapy Protocols Machine Learning Carcinoma, Pancreatic Ductal Sensitivity and Specificity Oxaliplatin Disease-Free Survival Male Pancreatic Neoplasms Deoxycytidine Neoplasm Proteins Gemcitabine Irinotecan
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