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Antineoplastic Combined Chemotherapy Protocols

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Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, for dr...

[A Case of Descending Colon and Rectal Cancer with Acute Myeloid Leukemia Performed Robot‒Assisted Hartmann's Procedure].

Gan to kagaku ryoho. Cancer & chemotherapy
The case is a 68‒year‒old male, who had been diagnosed with acute myeloid leukemia(AML)prior to rectal cancer surgery, was referred to our hospital for treatment in July 2019. We planned to treat the AML first, and then the colorectal cancer. After c...

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice ...

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.

Leukemia
Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrati...

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

Journal of translational medicine
BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding trea...

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.

Nature communications
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic...

Machine learning approaches for drug combination therapies.

Briefings in bioinformatics
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, to...

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network.

Computational and mathematical methods in medicine
Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural...