Abstract PR-02: Machine learning models to quantify lineage plasticity and neuroendocrine differentiation in high-grade prostate cancer

2021 ◽  
Author(s):  
Beatrice Knudsen ◽  
Isla Garraway ◽  
Arkadiusz Gertych ◽  
Michael Lewis ◽  
Alessandro Ferrero ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Neuroendocrine Differentiation ◽  
High Grade ◽  
Learning Models ◽  
Machine Learning Models

Machine-Learning Models for Multicenter Prostate Cancer Treatment Plans

2020 ◽  
Author(s):  
Khajamoinuddin Syed ◽  
William Sleeman ◽  
Payal Soni ◽  
Michael Hagan ◽  
Jatinder Palta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Treatment ◽  
Learning Models ◽  
Prostate Cancer Treatment ◽  
Treatment Plans ◽  
Machine Learning Models

scholarly journals Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1875
Author(s):  
Suhyun Hwangbo ◽  
Se Ik Kim ◽  
Ju-Hyun Kim ◽  
Kyung Jin Eoh ◽  
Chanhee Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Carcinoma ◽  
Model Performance ◽  
Ca 125 ◽  
Lymph Node Status ◽  
High Grade ◽  
Learning Models ◽  
Platinum Sensitivity ◽  
Serous Ovarian Carcinoma ◽  
Machine Learning Models

To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients’ clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.

scholarly journals Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1767 ◽  
Author(s):  
Piotr Woźnicki ◽  
Niklas Westhoff ◽  
Thomas Huber ◽  
Philipp Riffel ◽  
Matthias F. Froelich ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Prostate Gland ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Ability ◽  
Image Data ◽  
Learning Models ◽  
Potential Biomarker ◽  
Machine Learning Models

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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