Yuichiro Oishi et al. presented an interesting study reporting the ability of Artifi- cial Intelligence (AI) to diagnose and locate prostate cancer from multiparametric MRI (mpMRI) [1]. The authors evaluated the diagnostic performance of their AI with a ROC analysis; interestingly the area under the ROC curve was 0.985, while the sensitivity and the specificity were 0.875 and 0.961 (p < 0.01), respectively. Figure 1 of the paper shows that the regions of the prostate labeled by AI as prostate cancer correspond strictly to the cancer areas identified at pathological examination of the gland. These good results justified the strong conclusions of the paper: “diagnostic partition using the superpixel method and SVM-computed likelihood maps enables automated diagnosis of prostate cancer location and shape in mpMRI” [1]. Many aspects of this paper deserve to be emphasized. During the last two decades, numerous attempts to use radiomics for the diagnosis of cancer have been made [2]. So far, the dimensions of the dataset have always been a major limiting factor for the AI training and consequently for its diagnostic performance. The AI-based computer-aided diagnosis used in this study interestingly reached a good result with only a small number of patients, apparently overcoming the need for a large dataset. The authors achieved this result by sampling all the peripheral zone pixels for training the Support Vector Machine. Using this strategy, the dataset which resulted was very large despite the small number of patients included in the study. Because of the previous consideration, the strategy proposed by Yuichiro Oishi et al. will probably be crucial in the development of future diagnostic tools.

Using Artificial Intelligence for the Diagnosis of Prostate Cancer: The Paper of Yuichiro Oishi et al. Is a Step Forward on the Way of Precision Medicine

Michele Rizzo
;
Giovanni Liguori;Carlo Trombetta
2022

Abstract

Yuichiro Oishi et al. presented an interesting study reporting the ability of Artifi- cial Intelligence (AI) to diagnose and locate prostate cancer from multiparametric MRI (mpMRI) [1]. The authors evaluated the diagnostic performance of their AI with a ROC analysis; interestingly the area under the ROC curve was 0.985, while the sensitivity and the specificity were 0.875 and 0.961 (p < 0.01), respectively. Figure 1 of the paper shows that the regions of the prostate labeled by AI as prostate cancer correspond strictly to the cancer areas identified at pathological examination of the gland. These good results justified the strong conclusions of the paper: “diagnostic partition using the superpixel method and SVM-computed likelihood maps enables automated diagnosis of prostate cancer location and shape in mpMRI” [1]. Many aspects of this paper deserve to be emphasized. During the last two decades, numerous attempts to use radiomics for the diagnosis of cancer have been made [2]. So far, the dimensions of the dataset have always been a major limiting factor for the AI training and consequently for its diagnostic performance. The AI-based computer-aided diagnosis used in this study interestingly reached a good result with only a small number of patients, apparently overcoming the need for a large dataset. The authors achieved this result by sampling all the peripheral zone pixels for training the Support Vector Machine. Using this strategy, the dataset which resulted was very large despite the small number of patients included in the study. Because of the previous consideration, the strategy proposed by Yuichiro Oishi et al. will probably be crucial in the development of future diagnostic tools.
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https://www.mdpi.com/2673-4397/2/2/12
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/3025073
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