Radiomics prediction model for the improved diagnosis of  clinically significant prostate cancer on biparametric MRI

Li, Mengjuan; Chen, Tong; Zhao, Wei; Wei, Chaogang; Li, Xiaobo; Duan, Shaofeng; Ji, Libiao; Lu, Zhihua; Shen, Jing

doi:10.21037/qims.2019.12.06

Cited by 49 publications

(50 citation statements)

References 39 publications

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“…In a study by Li et al., patients with benign lesions and GS = 6 were grouped into clinically insignificant PCa. After univariate and multivariate logistic analysis, the results showed that age, tPSA, fPSA and clinical factors were important factors for predicting significantly important PCa, with an AUC value of 0.842 ( 24 ). Our study also showed that age and PSAD were important predictors of PCa.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, studies have confirmed that BPH is often characterized by the increased number of cells of different components, not only in the number of gland cells, but also in the number of smooth muscle cells, or may even consist entirely of stromal nodules ( 31 ). However, the pathological changes of PCa are mostly because of the increase of the number of cancer cells and the changes of extracellular space ( 24 ). Therefore, we speculated that the heterogeneity of cellular components may be the reason for the lower consistency of ultrasound radiomics in benign prostatic lesions than in PCa.…”

Section: Discussionmentioning

confidence: 99%

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A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

et al. 2021

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ObjectivesTo evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).MethodsA total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.ResultsThe multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.ConclusionsClinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

et al. 2021

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“…At least three mpMRI sequences are needed to diagnose PCa: T2-weighted imaging (T2WI), dynamic contrast-enhanced imaging (DCEI), and diffusion-weighted imaging (DWI). In the second edition of the Prostate Imaging Reporting and Data System (PI-RADS), DCEI plays a minor role, but DWI and its derived apparent diffusion coefficient (ADC) maps have become the main diagnostic parameters (6,7).…”

Section: Introductionmentioning

confidence: 99%

Efficacy of 68Ga-PSMA-11 PET/CT with biparametric MRI in diagnosing prostate cancer and predicting risk stratification: a comparative study

Nuo¹,

Li²,

Xue³

et al. 2022

Quant Imaging Med Surg

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Background: This retrospective study aimed to investigate the efficacy of the combined application of biparametric magnetic resonance imaging (bpMRI) and 68 Ga-PSMA-11 positron emission computed tomography/computed tomography (bpMRI/PET) in the qualitative diagnosis of intermediate-to high-risk prostate cancer (PCa). Methods:The 105 patients with suspected PCa included in the study underwent bpMRI and PET/CT. BpMRI examinations included conventional sequences and diffusion-weighted imaging (DWI) sequences.Major lesions were qualitatively diagnosed according to the Prostate Imaging Reporting and Data System (PI-RADS). A PET/CT scan was started 60 min after intravenous 68 Ga-PSMA-11 injection. The area with the highest radioactivity on PET/CT images was defined as the major lesion, and the maximum standard uptake value (SUV max ) was measured. All cases were confirmed by biopsy and pathology. Receiver operating characteristic curve (ROC) analysis was performed on the data to calculate sensitivity, specificity, and the Youden index.Results: Of the 105 patients, 68 patients were diagnosed with PCa, and 37 patients had benign prostatic lesions. With a PI-RADS score ≥3 as the diagnostic threshold, the accuracy of bpMRI in identifying benign and malignant prostate lesions was similar to that of PET/CT (SUV max threshold ≥10.9), and the Youden indices were 0.60 and 0.64, respectively. The sensitivity and specificity of bpMRI in the differential diagnosis of intermediate-to high-risk PCa versus low-risk PCa or benign lesions were 63% and 88%, respectively, and the Youden index was 0.51. With an SUV max ≥12.9 as the diagnostic threshold, the sensitivity and specificity of PET/CT in the differential diagnosis of intermediate-to high-risk PCa versus low-risk PCa or benign lesions were 74% and 94%, respectively, and the Youden index was 0.68. The sensitivity and specificity of bpMRI/PET in diagnosing PCa were 94% and 81%, respectively, and the Youden index was 0.75. The sensitivity and specificity of bpMRI/PET in the differential diagnosis of intermediate-to high-risk PCa versus low-risk PCa or benign lesions were 80% and 88%, respectively, and the Youden index was 0.68. Conclusions:The combined application of bpMRI and PET improves the accuracy of the qualitative diagnosis of prostate lesions, and its diagnostic efficacy for risk stratification in patients with intermediate-to 2 Nuo et al. A comparative study of PET/CT with MRI in PCa

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“…Radiomics (1)(2)(3)(4)(5) holds great promises in clinical oncology to aid better diagnosis, prognosis, and personalized clinical decision making, as witnessed by a huge number of research studies published in recent years (6)(7)(8)(9)(10)(11)(12)(13). However, the broad validity and generality of radiomics are much hindered by the concerns on its reliability (14)(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Yuan¹,

Xue²,

Lo³

et al. 2021

Quant Imaging Med Surg

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Background: MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT).Methods: An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations. Results:The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively.Conclusions: This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.

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Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI

Cited by 49 publications

References 39 publications

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

Efficacy of 68Ga-PSMA-11 PET/CT with biparametric MRI in diagnosing prostate cancer and predicting risk stratification: a comparative study

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

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