scholarly journals Machine learning based predictive model of early mortality in stage III and IV prostate cancer

2020 ◽  
Author(s):  
Robert Chen
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Survival ◽  
Stage Iv ◽  
Clinical Decision ◽  
Early Mortality ◽  
Stage Iii ◽  
Clinical Endpoints ◽  
Metabolomic Data ◽  
Metastatic Sites

AbstractProstate cancer remains the third highest cause of cancer-related deaths. Metastatic prostate cancer could yield poor prognosis, however there is limited work on predictive models for clinical decision support in stage III and IV prostate cancer.We developed a machine learning model for predicting early mortality in prostate cancer (survival less than 21 months after initial diagnosis). A cohort of 10,303 patients was extracted from the Surveillance, Epidemiology and End Results (SEER) program. Features were constructed in several domains including demographics, histology of primary tumor, and metastatic sites. Feature selection was performed followed by regularized logistic regression. The model was evaluated using 5-fold cross validation and achieved 75.2% accuracy with AUC 0.649. Of the 19 most predictive features, all of them were validated to be clinically meaningful for prediction of early mortality.Our study serves as a framework for prediction of early mortality in patients with stage II and stage IV prostate cancer, and can be generalized to predictive modeling problems for other relevant clinical endpoints. Future work should involve integration of other data sources such as electronic health record and genomic or metabolomic data.

scholarly journals Prostate Cancer Burden at the Uganda Cancer Institute

2016 ◽  
Vol 2 (4) ◽  
pp. 181-185 ◽  
Author(s):  
Fred Okuku ◽  
Jackson Orem ◽  
George Holoya ◽  
Chris De Boer ◽  
Cheryl L. Thompson ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Specific Antigen ◽  
Stage Iv ◽  
Cord Compression ◽  
Hormonal Manipulation ◽  
Number Of Patients ◽  
Stage Iv Disease ◽  
Metastatic Sites ◽  
Lost To Follow Up

Purpose In Uganda, the incidence of prostate cancer is increasing at a rate of 5.2% annually. Data describing presentation and outcomes for patients with prostate cancer are lacking. Methods A retrospective review of medical records for men with histologically confirmed prostate cancer at the Uganda Cancer Institute (UCI) from January 1 to December 17, 2012, was performed. Results Our sample included 182 men whose mean age was 69.5 years (standard deviation, 9.0 years). Patients who presented to the UCI had lower urinary tract symptoms (73%; n = 131), bone pain (18%; n = 32), increased prostate-specific antigen (PSA; 3%; n = 5), and other symptoms (6%; n = 11). Median baseline PSA was 91.3 ng/mL (interquartile range, 19.5-311.3 ng/mL), and 51.1% of the patients (n = 92) had a PSA value above 100 ng/mL. Gleason score was 9 or 10 in 66.7% of the patients (n = 120). Ninety percent (n = 136) had stage IV disease, and metastatic sites included bone (73%; n = 102), viscera (21%; n = 29), and lymph nodes (4%; n = 5). Spinal cord compression occurred in 30.9% (n = 55), and 5.6% (n = 10) experienced a fracture. A total of 14.9% (n = 27) underwent prostatectomy, and 17.7% (n = 32) received radiotherapy. Gonadotropin-releasing hormone agonist was given to 45.3% (n = 82), 29.2% (n = 53) received diethylstilbestrol, and 26% (n = 47) underwent orchiectomy. Chemotherapy was administered to 21.6% (n = 39), and 52.5% (n = 95) received bisphosphonates. During the 12 months of study, 23.8% of the men (n = 43) died, and 54.4% (n = 98) were lost to follow-up. Conclusion UCI patients commonly present with high PSA, aggressive Gleason scores, and stage IV disease. The primary treatments are hormonal manipulation and chemotherapy. Almost 25% of patients succumb within a year of presentation, and a large number of patients are lost to follow-up.

Evaluation of the Overall 5-Year Survival of Breast Cancer at INO: Data from the 5-Year Prospective Database

2020 ◽  
Vol 106 (1_suppl) ◽  
pp. 23-23
Author(s):  
NA Kaddi ◽  
NA Berrada ◽  
HA Errihani
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Metastatic Cancer ◽  
Stage Iv ◽  
Breast Cancer Survival ◽  
Stage I ◽  
Stage Ii ◽  
Stage Iii ◽  
Rank Test ◽  
Her2 Positive

Background: Breast cancer is both the most common and deadliest cancer among women in the world. The objective of this study was to assess breast cancer survival rates. Material and Methods: Prospective study conducted at the National Institute of oncology (INO) Sidi Mohamed Ben Abdellah Rabat .diagnosed patients with cancer from 2013 to 2015. The date of inclusion in the study is the date of histological confirmation of cancer. The survival assessment performed by the Kaplan Meier method, and the comparison between the different classes of a variable was performed by the Log Rank test. Results: 931 cases were collected during this study. According to molecular classification 59% of luminal patients, 25% positive human epidermal growth factor receptors (her2 positive) and 16% basal. The percentage of survival at 5 years, for luminal stage I 93%, stage II 92%, stage III 74% and stage IV 25% ;as well as for basal stage I 92%, stage II 80%, stage III 53% and stage IV 10% ; then the her2 positive stage I 100%, stage II 75% and stage III 70%. Conclusion: The discovery of metastatic cancer decreased breast cancer survival rate, hence the importance of Early Detection Awareness.

scholarly journals Machine Learning–Based Interpretation and Visualization of Nonlinear Interactions in Prostate Cancer Survival

2020 ◽  
pp. 637-646 ◽  
Author(s):  
Richard Li ◽  
Ashwin Shinde ◽  
An Liu ◽  
Scott Glaser ◽  
Yung Lyou ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Gleason Score ◽  
Cancer Survival ◽  
A Priori ◽  
Specific Antigen ◽  
Interaction Effects ◽  
Tree Model ◽  
Prediction Of Mortality ◽  
Staging Systems

PURPOSE Shapley additive explanation (SHAP) values represent a unified approach to interpreting predictions made by complex machine learning (ML) models, with superior consistency and accuracy compared with prior methods. We describe a novel application of SHAP values to the prediction of mortality risk in prostate cancer. METHODS Patients with nonmetastatic, node-negative prostate cancer, diagnosed between 2004 and 2015, were identified using the National Cancer Database. Model features were specified a priori: age, prostate-specific antigen (PSA), Gleason score, percent positive cores (PPC), comorbidity score, and clinical T stage. We trained a gradient-boosted tree model and applied SHAP values to model predictions. Open-source libraries in Python 3.7 were used for all analyses. RESULTS We identified 372,808 patients meeting the inclusion criteria. When analyzing the interaction between PSA and Gleason score, we demonstrated consistency with the literature using the example of low-PSA, high-Gleason prostate cancer, recently identified as a unique entity with a poor prognosis. When analyzing the PPC-Gleason score interaction, we identified a novel finding of stronger interaction effects in patients with Gleason ≥ 8 disease compared with Gleason 6-7 disease, particularly with PPC ≥ 50%. Subsequent confirmatory linear analyses supported this finding: 5-year overall survival in Gleason ≥ 8 patients was 87.7% with PPC < 50% versus 77.2% with PPC ≥ 50% ( P < .001), compared with 89.1% versus 86.0% in Gleason 7 patients ( P < .001), with a significant interaction term between PPC ≥ 50% and Gleason ≥ 8 ( P < .001). CONCLUSION We describe a novel application of SHAP values for modeling and visualizing nonlinear interaction effects in prostate cancer. This ML-based approach is a promising technique with the potential to meaningfully improve risk stratification and staging systems.

Postoperative radiotherapy treatment evolution in prostate cancer patients: A trend moving away from guidelines?

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 111-111
Author(s):  
Elisabeth E. Arrojo ◽  
Alvaro Martinez ◽  
Michael Ghilezan ◽  
Jeffrey D. Forman ◽  
Eduardo Fernandez
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Treatment Guidelines ◽  
Postoperative Radiotherapy ◽  
Scientific Evidence ◽  
Stage Iv ◽  
Stage Ii ◽  
Stage Iii ◽  
American Society ◽  
American Urological Association

111 Background: Randomized prospective clinical trials have demonstrated the benefit of adding postoperative radiotherapy (P-RT) for patients with prostate cancer with adverse pathological factors who underwent radical prostatectomy. Based on scientific evidence, the American Urological Association and American Society of Therapeutic Radiologists and Oncology, recommend offering P-RT to this type of patients. Methods: Retrospective analysis of 156,795 patients with prostate cancer diagnosed between 2004 and 2012, who underwent radical prostatectomy. The clinic-pathological information has been extracted from the database "Surveillance Epidemiology and End Results Program". Stage IV patients, and those with "Unknown" information on the type of surgery, radiotherapy, stage or grade, were excluded. Results: Stage II was the most common (81.9%), followed by stage III (18% of the patients). A 60% of the patients had Gleason > / = 8. Only 11.7% of the patients were > / = 70 years old. Treatment with P-RT decreased significantly between 2004 and 2012 (-0.44%; p = 0.0003). Analyzed by subgroups, P-RT decreased significantly in stage II (-0.72%; p < 0.0001) and III (-2.69%; p = 0.005), in patients with Gleason 6-7 (-1.05%; p = 0.0002) and > / = 8 (-1.79%; p = 0.0001) and < 70 years (-0.37%; p = 0.0001), whereas in > / = 70 years decreased but not significantly (-0.53%; p = 0.13). Conclusions: The use of P-RT in patients with prostate cancer who underwent radical prostatectomy is declining. What is most striking, is that the magnitude of this decline, is greatest in patients with higher risk factors, as the percentage of patients with Gleason score > / = 8 and/or Stage III (extracapsular invasion and/or involvement of seminal vesicles) receiving P-RT, declined more than the decline in the percentage of patients with Gleason 6-7 and/or stage II receiving P-RT, which clearly departs from the recommendations contained in the main treatment guidelines.

Stage IV Prostate Cancer: Survival Differences in Clinical T4, Nodal and Metastatic Disease

2010 ◽  
Vol 184 (2) ◽  
pp. 512-518 ◽  
Author(s):  
Wayland Hsiao ◽  
Kelvin A. Moses ◽  
Michael Goodman ◽  
Ashesh B. Jani ◽  
Peter J. Rossi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Metastatic Disease ◽  
Cancer Survival ◽  
Stage Iv ◽  
Survival Differences

scholarly journals Machine learning algorithm for early mortality prediction in patients with advanced penile cancer

2020 ◽  
Author(s):  
Robert Chen ◽  
Matthew R Kudelka ◽  
Aaron M Rosado ◽  
James Zhang
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Penile Cancer ◽  
Learning Algorithm ◽  
Stage Iv ◽  
The United States ◽  
Lymph Node Biopsy ◽  
Mortality Prediction ◽  
Early Mortality ◽  
Multivariate Logistic Regression Model

ABSTRACTPenile cancer remains a rare cancer with an annual incidence of 1 in 100,000 men in the United States, accounting for 0.4-0.6% of all malignancies. Furthermore, to date there are no predictive models of early mortality in penile cancer. Meanwhile, machine learning has potential to serve as a prognostic tool for patients with advanced disease.We developed a machine learning model for predicting early mortality in penile cancer (survival less than 11 months after initial diagnosis. A cohort of 88 patients with advanced penile cancer was extracted from the Surveillance, Epidemiology and End Results (SEER) program. In the cohort, patients with advanced penile cancer exhibited a median overall survival of 21 months, with the 25th percentile of overall survival being 11 months. We constructed predictive features based on patient demographics, staging, metastasis, lymph node biopsy criteria, and metastatic sites. We trained a multivariate logistic regression model, tuning parameters with respect to regularization, and feature selection criteria.Upon evaluation with 5-fold cross validation, our model achieved 68.2% accuracy with AUC 0.696. Criteria for advanced staging (T4, group stage IV), as well as higher age, white race and squamous cell histology, were the most predictive of early mortality. Tumor size was the strongest negative predictor of early mortality.Our study showcases the first known predictive model for early mortality in patients with advanced penile cancer and should serve as a framework for approaching the clinical problem in future studies. Future work should aim to incorporate other data sources such as genomic and metabolomic data, increase patient counts, incorporate clinical characteristics such as ECOG and RECIST criteria, and assess the performance of the model in a prospective fashion.

scholarly journals Prostate Cancer Survival and Mortality according to a 13-year retrospective cohort study in Brazil: Competing-Risk Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Sonia Faria Mendes Braga ◽  
Rumenick Pereira da Silva ◽  
Augusto Afonso Guerra Junior ◽  
Mariangela Leal Cherchiglia
Keyword(s):  
Prostate Cancer ◽  
Cancer Survival ◽  
Stage Iv ◽  
Competing Risk ◽  
Factors Associated ◽  
Risk Of Death ◽  
Unified Health System ◽  
Cancer Specific Mortality ◽  
Main Factors ◽  
Integrate Data

ABSTRACT: Objective: To analyze cancer-specific mortality (CSM) and other-cause mortality (OCM) among patients with prostate cancer that initiated treatment in the Brazilian Unified Health System (SUS), between 2002 and 2010, in Brazil. Methods: Retrospective observational study that used the National Oncological Database, which was developed by record-linkage techniques used to integrate data from SUS Information Systems, namely: Outpatient (SIA-SUS), Hospital (SIH-SUS), and Mortality (SIM-SUS). Cancer-specific and other-cause survival probabilities were estimated by the time elapsed between the date of the first treatment until the patients’ deaths or the end of the study, from 2002 until 2015. The Fine-Gray model for competing risk was used to estimate factors associated with patients’ risk of death. Results: Of the 112,856 studied patients, the average age was 70.5 years, 21% died due to prostate cancer, and 25% due to other causes. Specific survival in 160 months was 75%, and other-cause survival was 67%. For CSM, the main factors associated with patients’ risk of death were: stage IV (AHR = 2.91; 95%CI 2.73 - 3.11), systemic treatment (AHR = 2.10; 95%CI 2.00 - 2.22), and combined surgery (AHR = 2.30, 95%CI 2.18 - 2.42). As for OCM, the main factors associated with patients’ risk of death were age and comorbidities. Conclusion: The analyzed patients with prostate cancer were older and died mainly from other causes, probably due to the presence of comorbidities associated with the tumor.

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