Chemotherapy

Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application.

The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chemotherapy side effect remote monitoring web application. The F-measure was 0.79 for the medical concept extraction model and 0.85 for the negation extraction model (Bi-LSTM-CRF). The next step was the normalization. Of the 1040 unique concepts in the ...

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes.

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radi...

[Experience of Thoracotomy and Robot-assisted Bronchial Sleeve Resection 
after Neoadjuvant Chemoimmunotherapy for Local Advanced Central Lung Cancer].

BACKGROUND: Immunoneoadjuvant therapy opens a new prospect for local advanced lung cancer. The aim of our study was to explore the safety and feasibil...

[Deep Learning-based Risk Prediction Model for Postoperative Healthcare-associated Infections].

Objective To develop a risk prediction model combining pre/intraoperative risk factors and intraoperative vital signs for postoperative healthcare-ass...

Galvanostatically Deposited PtNi Thin-Films as Electrocatalysts for the Hydrogen Evolution Reaction.

The synthesis of hybrid platinum materials is fundamental to enable alkaline water electrolysis for cost-effective H generation. In this work, we have...

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical...

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced re...

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with ear...

Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study.

OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed...

Efficacy and safety of carboplatin with nab-paclitaxel versus docetaxel in older patients with squamous non-small-cell lung cancer (CAPITAL): a randomised, multicentre, open-label, phase 3 trial.

BACKGROUND: In Japan, docetaxel, a cytotoxic monotherapy, is the standard drug administered to older patients with advanced non-small-cell lung cancer...

Upstaging and Survival Outcomes for Non-Muscle Invasive Bladder Cancer After Radical Cystectomy: Results from the International Robotic Cystectomy Consortium.

We sought to describe the incidence, risk factors, and survival outcomes associated with pathologic upstaging from non-muscle invasive bladder cancer...

Transanal Minimally Invasive Surgery: A Useful Technique That Continues to Evolve.

Colorectal cancer remains the 3rd most common cancer diagnosed among men and women in the United States. With improved screening, premalignant rectal ...

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach.

PURPOSE: Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning...

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient progno...

[A Case of Robot-Assisted Radical Cystoprostatectomy for Locally Advanced Prostate Cancer with Bladder Infiltration].

While robot-assisted radical cystoprostatectomy (RARC) for locally advanced prostate cancer (LAPC) may sometimes prove to be excessive treatment, it c...

Micrometer-sized electrically programmable shape-memory actuators for low-power microrobotics.

Shape-memory actuators allow machines ranging from robots to medical implants to hold their form without continuous power, a feature especially advant...

[Lateral Port Site Hernia Following Robot Assisted Laparoscopic Radical Prostatectomy : A Case Report and Literature Review].

The patient was a 69-year-old man with localized cT1cN0M0 prostate cancer, who underwent robotassisted laparoscopic prostatectomy (RALP). The operatio...

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of...

Chemotherapy Knowledge Base Management in the Era of Precision Oncology.

Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Repre...

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological co...