askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men

Auffenberg, Gregory B.; Ghani, Khurshid R.; Ramani, Shreyas; Usoro, Etiowo; Denton, Brian T.; Rogers, Craig G.; Stockton, Benjamin R.; Miller, David C.; Singh, Karandeep; Collaborative, Michigan Urological Surgery Improvement

doi:10.1016/j.eururo.2018.09.050

Cited by 39 publications

(18 citation statements)

References 25 publications

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“…Thus this tool supports personalized decision making from multifactorial data sources. Alitto et al 38 ANN + MRI dx Tested machine learning classifiers for transition zone and peripheral zone in MRI to classify prostate tumors with or without a Gleason 4 component. Classifiers trained within each zone had higher performance than the subjected option of pathologists.…”

Section: Methodsmentioning

confidence: 99%

“…This registry, which is called askMUSIC, takes data from 45 urology practices within the Michigan Urological Surgery Improvement Collaborative (MUSIC). 38 This registry data is used to create a random forest machine learning model which could predict prostate cancer treatment options. Patients can go to askMUSIC website and interact with the registry data and predicted treatment to show therapy options to alleviate fear about a given therapy from the patient perspective.…”

Section: Artificial Neural Network and Histopathologic Diagnosis Of Pmentioning

confidence: 99%

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A Systematic Review of Artificial Intelligence in Prostate Cancer

Booven

Kuchakulla

Pai

et al. 2021

RRU

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Section: Methodsmentioning

confidence: 99%

Section: Artificial Neural Network and Histopathologic Diagnosis Of Pmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence in Prostate Cancer

Booven

Kuchakulla

Pai

et al. 2021

RRU

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“…Auffenberg et al [52] used a ML algorithm, trained with clinical and pathological variables such as age, medical history, PSA, Gleason scores, or number of positive cores, to guide patients in their choice of treatment and to help them understand their disease. This application also allowed urologists to guide their patients towards treatment or further examination by calculating the probability of organ-confined disease with a good accuracy (81%).…”

Section: Decision Makingmentioning

confidence: 99%

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

et al. 2020

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Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.

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“…In addition, owing to the growing necessity for developing a clinical decision support system (CDSS) that employs artificial intelligence (AI), the importance of predictive models using AI has been emphasized. Therefore, several researchers have attempted to develop predictive models using AI in their studies on prostate cancer (PCa) [ 1 – 3 ].…”

Section: Introductionmentioning

confidence: 99%

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

et al. 2020

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The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. Methods The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. Results Random forest exhibited good performance for TNM staging (recall value: 76.98%), while knearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline.

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askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men

Abstract: Background: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions.

Cited by 39 publications

References 25 publications

A Systematic Review of Artificial Intelligence in Prostate Cancer

A Systematic Review of Artificial Intelligence in Prostate Cancer

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

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