Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings

Karaarslan, Ercan; Kus, Aylin Altan; Aliş, Deniz; Karaarslan, Umut Can; Sağlıcan, Yeşim; Argun, Ömer Burak; Kural, Ali Rıza

doi:10.5152/dir.2022.20732

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…More specifically, seven out of ten features used by the best model were extracted from the ADC map, including the ADC mean, whose value was demonstrated to have a negative relationship with the probability of high-aggressive PCa ( Figure 6 and Supplementary Section S5 ). This is consistent with the literature since ADC values are demonstrated to be moderately correlated to GG of peripheral zone lesions [ 29 ] and to have a role in differentiating GG2 and PCa with higher grades [ 16 , 30 , 31 , 32 ]. Moreover, it was demonstrated that high-aggressive peripheral zone tumors are associated, on the ADC map, with high values of entropy and low values of energy [ 33 ].…”

Section: Discussionsupporting

confidence: 92%

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

Nicoletti,

Mazzetti,

Maimone

et al. 2024

Cancers

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In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.

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Section: Discussionsupporting

confidence: 92%

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

Nicoletti,

Mazzetti,

Maimone

et al. 2024

Cancers

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“…These results are consistent with those of our study. Similar conclusions about the sensitivity and specificity of ADC in differentiating the risk groups of patients with PCa have been reported in several studies ( 30 , 31 ).…”

Section: Discussionsupporting

confidence: 90%

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer

Qin¹,

Mu²,

Zheng³

et al. 2023

Quant Imaging Med Surg

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Background: More effective risk stratification of prostate cancer (PCa) than that possible with current methods can reduce undertreatment and guard against overtreatment. The aim of this study is to validate the differences and combined effects of amide proton transfer (APT) imaging and apparent diffusion coefficient (ADC) in discriminating the PCa grade group (GG) ≤2 from GG ≥3 PCa. Methods: This is an ongoing prospective study conducted in the radiology department of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. Patients pathologically diagnosed with PCa were enrolled consecutively according to the eligibility criteria. A total of 180 patients (age range, 42-92 years) were included in this study. Using histopathology as the reference standard, we placed 71 cases in GG ≤2 (mean age 67.03±8.696 years) and 109 cases in GG ≥3 (age 69.60±9.638 years). Magnetic resonance imaging (MRI) parameters, including APT and ADC values, were analyzed using an independent samples t-test and binary logistic regression analysis stratified with GG. Receiver operating characteristic curve was used to analyze the diagnostic performance for different parameters distinguishing GG ≤2 and GG ≥3. Results: APT [odds ratio (OR) for the transitional zone (TZ)

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Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis

Agrotis,

Pooch,

Abdelatty

et al. 2024

Eur Radiol

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Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings

Abstract: apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings.

Cited by 5 publications

References 25 publications

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer

Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis

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