AIMC Topic: Disease-Free Survival

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A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BMC cancer Sep 5, 2019
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
Proportional Hazards Models Biomarkers, Tumor Multivariate Analysis Male Humans Kaplan-Meier Estimate Middle Aged Genes, Neoplasm Disease Progression Adenocarcinoma of Lung Machine Learning Transcriptome Lung Neoplasms Prognosis Gene Expression Regulation, Neoplastic Female Follow-Up Studies Aged Databases, Genetic Survival Rate Disease-Free Survival
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Robust identification of molecular phenotypes using semi-supervised learning.

BMC bioinformatics May 28, 2019
BACKGROUND: Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised ...
Supervised Machine Learning Female Humans Disease-Free Survival Algorithms Breast Neoplasms Databases as Topic Phenotype RNA, Messenger Chemotherapy, Adjuvant Prognosis Gene Expression Regulation, Neoplastic Lymphoma
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Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands) Apr 15, 2019
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...
Disease-Free Survival Proportional Hazards Models Multivariate Analysis Mitosis Humans Algorithms Prognosis Female Middle Aged Deep Learning Triple Negative Breast Neoplasms
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Oncologic outcomes in patients treated with endoscopic robot assisted simple enucleation (ERASE) for renal cell carcinoma: Results from a tertiary referral center.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Apr 4, 2019
INTRODUCTION: Open Simple Enucleation (OSE) has been demonstrated to be an oncologically safe alternative to standard partial nephrectomy. We assessed the mid-term oncologic outcomes and predictors of disease recurrence in patients treated with Endos...
Humans Retrospective Studies Tomography, X-Ray Computed Female Aged Magnetic Resonance Imaging Robotics Laparoscopy Tertiary Care Centers Italy Survival Rate Male Middle Aged Treatment Outcome Follow-Up Studies Margins of Excision Disease-Free Survival Kidney Neoplasms Carcinoma, Renal Cell Nephrectomy
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Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

PloS one Feb 20, 2019
Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuv...
Male Middle Aged Urinary Bladder Neoplasms Models, Biological Disease-Free Survival Cystectomy Databases, Factual Humans Female Machine Learning Aged Aged, 80 and over Survival Rate
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Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.

Scientific reports Jan 31, 2019
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inacc...
Humans Algorithms Databases, Factual Female Disease-Free Survival Diffusion Tensor Imaging Middle Aged Aged Deep Learning Adult Adolescent Survival Rate Glioma Brain Neoplasms Neoplasm Grading Male
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The oncological and surgical safety of robot-assisted surgery in colorectal cancer: outcomes of a longitudinal prospective cohort study.

Surgical endoscopy Jan 28, 2019
BACKGROUND: Colorectal cancer is one of the most common cancers worldwide. Laparoscopic colorectal surgery (LCRS) is a frequently used modality. A new development in minimally invasive surgery is robot-assisted colorectal surgery (RACRS).
Colectomy Margins of Excision Intraoperative Complications Robotics Length of Stay Humans Neoplasm Staging Incidence Prospective Studies Neoplasm Recurrence, Local Follow-Up Studies Female Disease-Free Survival Operative Time Aged Netherlands Survival Rate Colorectal Neoplasms Male Postoperative Complications
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Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 1, 2018
BACKGROUND AND PURPOSE: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperat...
Kaplan-Meier Estimate Humans Precision Medicine Cystadenocarcinoma, Serous Prognosis Cohort Studies Ovarian Neoplasms Tomography, X-Ray Computed Predictive Value of Tests Female Neoplasm Recurrence, Local Biomarkers, Tumor Disease-Free Survival Middle Aged Deep Learning
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Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

EBioMedicine Oct 22, 2018
BACKGROUND: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from dr...
Myelodysplastic Syndromes Neoplasm, Residual Humans Male Machine Learning Predictive Value of Tests Female Flow Cytometry Survival Rate Disease-Free Survival Leukemia, Myeloid, Acute
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Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

Breast cancer research and treatment Sep 7, 2018
PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.
Models, Theoretical Disease-Free Survival Receptor, ErbB-2 Trastuzumab Chemotherapy, Adjuvant Humans Aged Adult Breast Neoplasms Machine Learning Brain Neoplasms Retrospective Studies Middle Aged Female
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