51
|
Lin YT, Lee MTS, Huang YC, Liu CK, Li YT, Chen M. Prediction of Recurrence-associated Death from Localized Prostate Cancer with a Charlson Comorbidity Index-reinforced Machine Learning Model. Open Med (Wars) 2019; 14:593-606. [PMID: 31428684 PMCID: PMC6698054 DOI: 10.1515/med-2019-0067] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 06/06/2019] [Indexed: 12/28/2022] Open
Abstract
Research has failed to resolve the dilemma experienced by localized prostate cancer patients who must choose between radical prostatectomy (RP) and external beam radiotherapy (RT). Because the Charlson Comorbidity Index (CCI) is a measurable factor that affects survival events, this research seeks to validate the potential of the CCI to improve the accuracy of various prediction models. Thus, we employed the Cox proportional hazard model and machine learning methods, including random forest (RF) and support vector machine (SVM), to model the data of medical records in the National Health Insurance Research Database (NHIRD). In total, 8581 individuals were enrolled, of whom 4879 had received RP and 3702 had received RT. Patients in the RT group were older and exhibited higher CCI scores and higher incidences of some CCI items. Moderate-to-severe liver disease, dementia, congestive heart failure, chronic pulmonary disease, and cerebrovascular disease all increase the risk of overall death in the Cox hazard model. The CCI-reinforced SVM and RF models are 85.18% and 81.76% accurate, respectively, whereas the SVM and RF models without the use of the CCI are relatively less accurate, at 75.81% and 74.83%, respectively. Therefore, CCI and some of its items are useful predictors of overall and prostate-cancer-specific survival and could constitute valuable features for machine-learning modeling.
Collapse
Affiliation(s)
- Yi-Ting Lin
- Department of Urology, St. Joseph Hospital, Yunlin County, 63241, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| | - Michael Tian-Shyug Lee
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| | - Yen-Chun Huang
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| | - Chih-Kuang Liu
- Department of Urology, St. Joseph Hospital, Yunlin County, 63241, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| | - Yi-Tien Li
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| | - Mingchih Chen
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City24205, Taiwan
| |
Collapse
|
52
|
Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, Brunetti A, Imbriaco M. Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 2019; 3:35. [PMID: 31392526 PMCID: PMC6686027 DOI: 10.1186/s41747-019-0109-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 07/02/2019] [Indexed: 12/17/2022] Open
Abstract
With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its 'black box' nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.
Collapse
Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Maria Brunella Cipullo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy.
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Leonardo Radice
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| |
Collapse
|
53
|
Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 2019; 25:183-188. [PMID: 31063138 PMCID: PMC6521904 DOI: 10.5152/dir.2019.19125] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/08/2019] [Accepted: 03/23/2019] [Indexed: 01/30/2023]
Abstract
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
Collapse
Affiliation(s)
- Stephanie A. Harmon
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Sena Tuncer
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Thomas Sanford
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Peter L. Choyke
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Barış Türkbey
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| |
Collapse
|