Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment

Liu, Xiang; Wang, Xiangpeng; Zhang, Yaofeng; Sun, Zhaonan; Zhang, Xiaodong; Wang, Xiaoying

doi:10.1007/s00261-022-03583-5

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…Table 2 summarizes the 11 studies utilizing MRI-derived radiomic models to predict EPE, PSM, or LNI [ 25 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Seven [ 25 , 31 , 32 , 33 , 38 , 39 ], six [ 25 , 34 , 35 , 36 , 37 , 39 ], and one [ 38 ] study(ies) reported on EPE, LNI, and PSM, respectively. Uniquely, Damascelli et al utilized both EPE and LNI [ 25 ] as their primary endpoint, while He and colleagues utilized both PSM and EPE [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

“…The ROC-AUC on training sets ranged from 0.674 to 0.92, while the ROC-AUC on testing sets ranged from 0.598 to 0.92. For studies predicting LNI, the prostate [ 34 , 37 , 39 ], PI-RADS lesions [ 35 , 37 ], and index lesions [ 25 , 36 ] were used as the regions of interest. The ROC-AUC on training sets ranged from 0.82 to 1.0, and the ROC-AUC for testing sets ranged from 0.73 to 0.94.…”

Section: Resultsmentioning

confidence: 99%

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The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

Huynh

Hwang²,

Taylor

et al. 2023

Diagnostics

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The development of precise medical imaging has facilitated the establishment of radiomics, a computer-based method of quantitatively analyzing subvisual imaging characteristics. The present review summarizes the current literature on the use of diagnostic magnetic resonance imaging (MRI)-derived radiomics in prostate cancer (PCa) risk stratification. A stepwise literature search of publications from 2017 to 2022 was performed. Of 218 articles on MRI-derived prostate radiomics, 33 (15.1%) generated models for PCa risk stratification. Prediction of Gleason score (GS), adverse pathology, postsurgical recurrence, and postradiation failure were the primary endpoints in 15 (45.5%), 11 (33.3%), 4 (12.1%), and 3 (9.1%) studies. In predicting GS and adverse pathology, radiomic models differentiated well, with receiver operator characteristic area under the curve (ROC-AUC) values of 0.50–0.92 and 0.60–0.92, respectively. For studies predicting post-treatment recurrence or failure, ROC-AUC for radiomic models ranged from 0.73 to 0.99 in postsurgical and radiation cohorts. Finally, of the 33 studies, 7 (21.2%) included external validation. Overall, most investigations showed good to excellent prediction of GS and adverse pathology with MRI-derived radiomic features. Direct prediction of treatment outcomes, however, is an ongoing investigation. As these studies mature and reach potential for clinical integration, concerted effort to validate these radiomic models must be undertaken.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

Huynh

Hwang²,

Taylor

et al. 2023

Diagnostics

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“…In their recent paper, Liu X. et al developed and tested radiomics models b ADC maps for pelvic LNI prediction in PCa. The radiomics model proposed that an automatically segmented prostate mask achieved a higher area under the curve with a value of 0.73, compared to the Briganti 2017, the MSKCC nomograms, and RADS assessment models, which showed AUCs of 0.69, 0.71, and 0.70, respectivel results showed comparable accuracy of the radiomics model proposed to the MKSCC and Briganti 2017 [17].…”

Section: Mpmrimentioning

confidence: 89%

Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review

Faiella,

Vaccarino,

Ragone

et al. 2023

JCM

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(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI’s role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools’ effectiveness in this area.

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“…In view of those disadvantages, some researches had used 18F-PSMA-PET-CT to evaluate lymph nodes disease 10 – 13 , and the control group was pelvic CT. However, MRI which is also a conventional image, seems to be more accurate than CT 14 .…”

Section: Introductionmentioning

confidence: 97%

Diagnostic capability of 18F-PSMA PET-MRI and pelvic MRI plus bone scan in treatment-naive prostate cancer: a single-center paired validating confirmatory study

Qiao,

Wang,

Wang

et al. 2023

International Journal of Surgery

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Background: Radical prostatectomy with pelvic lymph node dissection for the treatment of high-risk localized prostate cancer (PCa) results in long-term benefits in selected patients. But insufficient sensitivity of conventional examinations which are pelvic MRI and bone scan, limits the diagnosis of bone and lymph node metastasis of PCa. This affects the surgical management strategy of a large number of patients. The purpose of this study was to investigate whether 18F-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)-magnetic resonance imaging (MRI) could improve the clinical detection of PCa metastases compared with the conventional pelvic MRI plus bone scan. Materials and Methods: From April 2020 to April 2023, we prospectively enrolled 472 patients with histologically proven PCa in our center, and 120 patients underwent 18F-PSMA PET-MRI, multiparametric MRI (mpMRI), and bone scan before laparoscopic radical prostatectomy plus lymph node dissection. The accuracy of imaging results in detecting lymph node and bone metastatic lesions was compared between PSMA PET-MRI and MRI plus bone scan. Results: In diagnosing lymph node metastasis, PSMA PET-MRI had an area under the curve (AUC) of 0.844 (95% confidence intervals [CI]: 0.738–0.949, P < 0.001), sensitivity and specificity of 75% and 96%, which performed apparently better than MRI (AUC=0.615 [95% CI: 0.480–0.750, P=0.073]). PSMA PET-MRI showed excellent expression in the diagnosis of bone metastases, with an AUC of 0.910 (95% CI: 0.840–0.981, P <0.001) compared to 0.700 (95% CI: 0.577–0.823, P =0.001) in bone scanning. PSMA PET-MRI also had higher sensitivity than bone scanning (90% vs. 43%), while lower specificity (92% vs. 97%). Conclusion: PSMA PET-MRI is superior to conventional imaging at diagnosing metastases in lymph nodes and bones in PCa and can provide a more accurate stagement.

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Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment

Cited by 7 publications

References 36 publications

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review

Diagnostic capability of 18F-PSMA PET-MRI and pelvic MRI plus bone scan in treatment-naive prostate cancer: a single-center paired validating confirmatory study

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