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We proposed the PCAIDS, an AutoML-based model, for the prediction of csPCa based on quick and economic routinely performed clinical examinations. The PCAIDS incorporated multimodal and multidimensional data, including laboratory tests, imaging tests, and demographic data, revealing encouraging discriminative power with AUCs of 0.820 in the validation cohort and 0.807 and 0.850 in the two prospective test cohorts.

Compared with previous prediction models, such as the ERSPC-RC [14], PCPT-RC [15] and CPCC-RC [16], the PCAIDS, for the first time, evaluated over 100 multimodal features with AI-based algorithms. These features, including demographics, laboratory tests, and imaging examinations, were assessed by a series of AI algorithms. Among these AI algorithms, AutoML outperformed logistic regression, random forest, and XGBoost. AutoML has become a popular and efficient modeling tool for data science that uses k-fold cross-validation through varying optimization algorithms, such as grid search, random search, and genetic algorithm (GA), to scan different feature combinations, feature transformations, supervised algorithms, and their corresponding hyperparameter combinations implemented in AutoWEKA [17], Autogluon [18], AutoSklearn 2.0 [19], and TPOT, [20] thereby identifying the optimal machine learning pipeline.

Additionally, AI-based methods have the potential to analyze high-volume data and to discover nonlinear and interactive prediction information. For cancer diagnosis, there were huge possibilities that currently applied predictive models only included a proportion of effective predictors. Although the application of AI-based methods may not always outperform linear models, the advantage of involving more features could help the models to be more stable and more applicable for different populations.

In this aspect, Jungyo Suh et al. proposed the possibility of applying AI-based algorithms in the prediction of prostate biopsy. They developed an AI-based prediction tool with PSA, total prostate volume, age, hypoechoic lesion on ultrasonography, transitional zone volume, testosterone, and fPSA [21]. This study showed the promising future of using AI-based algorithms in predicting PCa; however, the investigated features were of limited number. To some extent, AI-based algorithms were not ideal for the analysis of limited features, which could have been done by traditional methods. In predicting colon cancer, researchers applied AI-based methods to data from health maintenance organizations by evaluating analytes from standard laboratory records, including hematology, liver function, and metabolism [22]. In breast cancer, the notion of applying AI-based methods to diagnose breast cancer was validated, and age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used in the prediction model [23]. Further studies validated that routine blood analysis features had a boosted performance for breast cancer diagnosis and supported the notion that this approach is of great potential to be used in a widespread manner to detect cancers [24]. These studies suggested the possibility of using routine health examinations to predict cancer based on AI algorithms.

The clinical scenario for the application of PCAIDS is between PSA-based screening and novel tests predicting PCa, including mpMRI, urinary PCA3 test, 4kScore, and Prostate Health Index. MpMRI, a potent modality in predicting biopsy results, is of great importance in patients who are at high risk of PCa. However, the application of mpMRI is limited by the accessibility of MRI machines and the professionalism of the radiologists who interpret the images. Meanwhile, these biomarkers were only available for patients in some countries and regions. In addition, mpMRI and these novel biomarkers are associated with high costs in most countries. The application of PCAIDS, on the other hand, does not require special examination equipment. The features included in the model were common, routinely performed, quick, and economic tests, which were also needed for a general health check-up. The application of B-ultrasound in evaluating the size of the prostate is also accessible for almost every hospital. In general, this AI-based modality is not here to perfect the diagnostic modality with mpMRI and novel biomarkers, rather than replacing them.

AutoML has the flaw of interpretability, which is consistently met with skepticism, similar to other complex models, especially in the medical field. To this end, we applied the SHAP [13] tool to explore the contribution of individual features to the model. To explore the rationality of this contribution, we also examined the interpretability of the LR compared to SHAP (Additional file4: Figure S1). First, the contribution of the key variables (the cross-sectional area of the prostate (B_AREA), AGE, and fPSA) is basically the same in the two prediction modalities. This is similar to the previous conclusions obtained by the RF model (Additional file1: Table S1). Second, the SHAP value from AutoML is roughly the same as the importance of LR calculated by model coefficients. Third, B_AREA is the most important variable. Significantly, the risk of PCa did not increase with B_AREA, which may be due to the increased concertation of PSA produced by a larger prostate, misstating that the risk of PCa and fPSA/tPSA are similar. In addition, age played the second most important role in the prediction model. Thereafter, the risk of PCa increases with age, which is intuitive, and the same holds true for other clinical indicators, although no direct cause can be inferred.

One of the limitations of this study is the lack of a head-to-head comparison with mpMRI or other novel biomarkers. However, the clinical scenario of this prediction mode is not to replace novel diagnostic methods but to assist in decision-making for novel diagnostic methods. In addition, we introduced the dimensions of the prostate from the B ultrasound in the model, and there might be inter- and intrarater differences among different centers in terms of ultrasound results. Furthermore, ultrasound images were not included in this study due to the lack of image storage in all centers. We believe that future studies may incorporate the images captured during ultrasound examinations. The findings of this study are applicable primarily to Asian populations due to the vast discrepancy between Asian and Caucasian patients. In the future, we intend to collect data from various populations to adapt our model to different ethnic groups. Finally, the performance of the PCAIDS is not better than that of the other algorithms, including LR. However, it is important to note that in the study, given the serious implications of missing a prostate cancer diagnosis, prioritizing sensitivity rather than specificity was chosen. This decision was made understanding that it might increase the false positive rate, but it's a reasonable trade-off given the potential severity of a missed diagnosis, where high sensitivity can often lead to lower specificity. We consider that further validation studies may help us to show its wide applicability.

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Artificial intelligence for the diagnosis of clinically significant prostate ... - BMC Medicine