Next Article in Journal
A Review of Trastuzumab Biosimilars in Early Breast Cancer and Real World Outcomes of Neoadjuvant MYL-1401O versus Reference Trastuzumab
Previous Article in Journal
Cancer Patients’ Experiences with Telehealth before and during the COVID-19 Pandemic in British Columbia
Previous Article in Special Issue
Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer
Article

Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

1
Department of Urology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
2
Department of Urology, “Pius Brinzeu” Clinical Emergency County Hospital, 300736 Timisoara, Romania
3
Department of Computer Science, West University, 300223 Timisoara, Romania
4
Department of Communications, Polytechnic University, 300006 Timisoara, Romania
5
Department of Pathology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
6
Department of Gastroenterology, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Curr. Oncol. 2022, 29(6), 4212-4223; https://doi.org/10.3390/curroncol29060336
Received: 21 April 2022 / Revised: 5 June 2022 / Accepted: 8 June 2022 / Published: 10 June 2022
(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms. View Full-Text
Keywords: artificial intelligence system; shear wave elastography; prostate cancer artificial intelligence system; shear wave elastography; prostate cancer
Show Figures

Figure 1

MDPI and ACS Style

Secasan, C.C.; Onchis, D.; Bardan, R.; Cumpanas, A.; Novacescu, D.; Botoca, C.; Dema, A.; Sporea, I. Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements. Curr. Oncol. 2022, 29, 4212-4223. https://doi.org/10.3390/curroncol29060336

AMA Style

Secasan CC, Onchis D, Bardan R, Cumpanas A, Novacescu D, Botoca C, Dema A, Sporea I. Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements. Current Oncology. 2022; 29(6):4212-4223. https://doi.org/10.3390/curroncol29060336

Chicago/Turabian Style

Secasan, Ciprian C., Darian Onchis, Razvan Bardan, Alin Cumpanas, Dorin Novacescu, Corina Botoca, Alis Dema, and Ioan Sporea. 2022. "Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements" Current Oncology 29, no. 6: 4212-4223. https://doi.org/10.3390/curroncol29060336

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop