Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

Zhang, Kevin Sun; Schelb, Patrick; Kohl, Simon A. A.; Radtke, Jan Philipp; Wiesenfarth, Manuel; Schimmöller, Lars; Kuder, Tristan Anselm; Hohenfellner, Markus; Maier‐Hein, Klaus H.; Bonekamp, David

doi:10.1016/j.mri.2021.06.013

Cited by 22 publications

(21 citation statements)

References 28 publications

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“…It is suggested that in addition to ADC image texture and shape information, its own features and ADC values also have certain diagnostic value. The research of Xu ( 26 ) and Zhang ( 27 ) has proven this conjecture.…”

Section: Discussionmentioning

confidence: 81%

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions

Kang

et al. 2022

Front. Oncol.

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ObjectiveTo compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.MethodsA total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.ResultsIn the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.ConclusionThe results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.

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Section: Discussionmentioning

confidence: 81%

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions

Kang

et al. 2022

Front. Oncol.

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“…Finally, texture analysis could further stratify PI-RADS 3 lesions into indolent or clinically significant PCa (csPCa) nodules, thus avoiding unnecessary biopsies [ 12 , 19 , 21 ]. The paper published by Hectors et al [ 12 ] showed a sensitivity and specificity of 75% and 79.6%, respectively, with an AUC of 0.76 regarding the detection of csPCa among PI-RADS 3 lesions ( p = 0.022), while mpMRI alone registered a sensitivity of 38.24% and a specificity of 53.85% for PI-RADS 3 tumors [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

Telecan

Andraș

Crişan

et al. 2022

JPM

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(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

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“…It has been shown that the diagnostic accuracy of prostate MRI examinations can be improved by considering PSA density, quantitative image criteria such as mean ADC values and radiomic features, as well as by using new techniques like MR fingerprinting and machine learning (11,13,(20)(21)(22)(23)(24). Furthermore, 68Ga-PSMA PET/CT also showed good accuracy in the primary diagnosis of csPCa and could be used additionally in case of uncertainty whether a biopsy should be performed or not (25) CDR of PI-RADS categories ≤2 are very low, independently from PSAD (4).…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al proposed a downgrading algorithm for PI-RADS ≥4 lesions with an mADC cut-off at a threshold value of 732 μm 2 /s (13). By applying this downgrading algorithm, they achieved to increase specificity and accuracy without sacrificing sensitivity for detection of csPCa (ISUP >1) in their patient cohort.…”

Section: External Validation Of Proposed Madc-cutoff Values/algorithmsmentioning

confidence: 99%

“…Recent studies have shown that assessing mpMRI quantitatively using radiomic features and mean ADC (apparent diffusion coefficient) values can improve diagnostic accuracy regarding TZ lesions compared to qualitative PI-RADS reading alone (11)(12)(13). For example, improving specificity and preserving sensitivity is feasible by downgrading PI-RADS lesions ≥4 based on mean ADC values or machine learning algorithms (13).…”

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confidence: 99%

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Quantitative Analysis of Diffusion Weighted Imaging May Improve Risk Stratification of Prostatic Transition Zone Lesions

Engel

Oerther

Reisert

et al. 2022

In Vivo

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Background/Aim: To investigate whether quantitative analysis of diffusion weighted images allows for improved risk stratification of transition zone lesions in prostate magnetic resonance imaging (MRI) evaluated according to target variable: clinically significant prostate cancer (csPCa)]. Patients and Methods: Consecutive patients with transition zone lesions in 3T prostate MRI were enrolled in the study. All lesions on MRI were histopathologically verified by transperineal MRI-TRUS fusion biopsy. Two blinded radiologists re-evaluated all lesions according to PI-RADSv2.1. A consensus reading was performed after reading of all cases. Additionally, mean apparent diffusion coefficient values (mADC) were derived from blinded lesion segmentation. ROC analysis was performed for PI-RADS categories and PI-RADS categories with separate subcategories and diffusion coefficient values (ADC). Data were examined for optimal mADC cut-off values that improve stratification of csPCa and benign lesions. Results: Among 85 patients (mean age=66.2 years), 98 transition zone lesions were detected. Biopsy confirmed csPCa in 24/98 cases. Area under the curve (AUC) was 0.89/0.90 for reader 1, 0.92/0.91 for reader 2 and 0.92/0.91 for the consensus reading (5 category analysis/analysis with subcategories separately). Inter-reader agreement was substantial, with lower PI-RADS categories assigned by the more experienced reader (p<0.05). AUC for mADC alone was 0.81. When a cut-off threshold of 950 μm 2 /s mADC is used to downgrade PI-RADS 3 lesions to PI-RADS 2, biopsy could be avoided in all benign PI-RADS 3 cases. Conclusion: Quantitative analysis of diffusion weighted images may help avoid unnecessary biopsies of transition zone PI-RADS 3 lesions.Multiparametric magnetic resonance imaging of the prostate (mpMRI) has been established as the de facto standard for imaging assessment of possible prostate cancer (PCa). It is implemented in European and American guidelines for diagnosis in biopsy-naive patients, patients with prior negative biopsy and for PCa patients before inclusion into active surveillance (1, 2). Prostate lesions are classified into five categories according to the latest Prostate Imaging Reporting and Data System lexicon (PI-RADS), version 2.1 (3). Higher categories have higher probabilities to detect clinically significant cancer. The cancer detection rates (CDR) for each category do not significantly differ between the peripheral zone (PZ) or the transition zone (TZ), being very low (<5%) for clinically significant prostate cancer (International Society 2323

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Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

Cited by 22 publications

References 28 publications

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

Quantitative Analysis of Diffusion Weighted Imaging May Improve Risk Stratification of Prostatic Transition Zone Lesions

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