Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions

Nai, Ying-Hwey; Cheong, Dennis Lai Hong; Roy, Sharmili; Kok, Trina; Stephenson, Mary C.; Schaefferkoetter, Josh; Totman, John J.; Conti, Maurizio; Eriksson, Lars; Robins, Edward G.; Wang, Ziting; Chua, Wynne; Ang, Bertrand; Singha, Arvind Kumar; Thamboo, Thomas Paulraj; Chiong, Edmund; Reilhac, Anthonin

doi:10.1016/j.mri.2023.03.009

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Within this new framework, many problems and solutions in this field were recognized (e.g., better methods for feature selection such as genetic algorithms, avoiding of 'overlearning/overfitting' by n-fold crossvalidation, early stopping techniques, L2-regulation techniques and validating the model in independent test samples, 'dimensionality reduction', application of a 'reject option' to increase negative and positive predictive power at the cost that not every patient receives a prediction) and a bulk of methods and models were found in order to cope with these problems [17]. All these new methods resulted in machine learning algorithms and deep learning analysis which were finally successfully applied in various subdisciplines in urology and nephrology [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms

Huettenbrink

Hitzl

Distler

et al. 2023

JPM

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The prediction of radiation exposure is an important tool for the choice of therapy modality and becomes, as a component of patient-informed consent, increasingly important for both surgeon and patient. The final goal is the implementation of a trained and tested machine learning model in a real-time computer system allowing the surgeon and patient to better assess patient’s personal radiation risk. In summary, 995 patients with ureterorenoscopy over a period from May 2016 to December 2019 were included. According to the suggestions based on actual literature evidence, dose area product (DAP) was categorized into ‘low doses’ ≤ 2.8 Gy·cm2 and ‘high doses’ > 2.8 Gy·cm2 for ureterorenoscopy (URS). To forecast the level of radiation exposure during treatment, six different machine learning models were trained, and 10-fold crossvalidated and their model performances evaluated in training and independent test samples. The negative predictive value for low DAP during ureterorenoscopy was 94% (95% CI: 92–96%). Factors influencing the radiation exposure were: age (p = 0.0002), gender (p = 0.011), weight (p < 0.0001), stone size (p < 0.000001), surgeon experience (p = 0.039), number of stones (p = 0.0007), stone density (p = 0.023), use of flexible endoscope (p < 0.0001) and preoperative stone position (p < 0.00001). The machine learning algorithm identified a subgroup of patients of 81% of the total sample, for which highly accurate predictions (94%) were possible allowing the surgeon to assess patient’s personal radiation risk. Patients without prediction (19%), the medical expert can make decisions as usual. Next step will be the implementation of the trained model in real-time computer systems for clinical decision processes in daily practice.

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Section: Introductionmentioning

confidence: 99%

Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms

Huettenbrink

Hitzl

Distler

et al. 2023

JPM

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show abstract

“…They are thought to capture distinct phenotypic differences in tumors and quantitatively assess intra- and intertumoral heterogeneity [ 18 , 19 ]. Recent radiomic feature analyses in prostate cancer research have assessed their ability to differentiate low from higher-grade prostate cancer [ 20 , 21 , 22 , 23 ], predict Gleason Grades [ 24 , 25 , 26 ], and identify tumor presence [ 18 , 19 ]. Radiomic feature calculations are highly sensitive to variations in image acquisition and often sacrifice the interpretability of these mathematical representations of image characteristics; however, if properly used as inputs to machine and deep learning models, they can non-invasively identify relationships and potential biomarkers of a myriad of diseases [ 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Duenweg,

Bobholz,

Barrett

et al. 2023

Cancers

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Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

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Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

Khateri,

Babapour Mofrad,

Geramifar

et al. 2024

Phys Eng Sci Med

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Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions

Cited by 5 publications

References 28 publications

Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms

Personalized Prediction of Patient Radiation Exposure for Therapy of Urolithiasis: An Application and Comparison of Six Machine Learning Algorithms

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

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