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Contains fulltext : 287809.pdf (Publisher’s version ) (Open Access), OBJECTIVES: To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa). METHODS: This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS >/= 2 lesions across prior and current studies. The heatmaps for each patient's prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores. RESULTS: The model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81). CONCLUSIONS: Our proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy. KEY POINTS: * Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. * The diagnostic accuracy of our csPCa detection AI model improved by including c

Contains fulltext : 297141.pdf (Publisher’s version ) (Closed access)

Contains fulltext : 283301.pdf (Publisher’s version ) (Open Access), OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI). MATERIALS AND METHODS: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis. RESULTS: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]). CONCLUSIONS: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%. KEY POINTS: * Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. * Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. * Applying deep learning to an auto-fixed VOI radiomics approach can

Contains fulltext : 239809.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 219672.pdf (Publisher’s version ) (Open Access), OBJECTIVES: To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). METHODS: This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. RESULTS: The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. CONCLUSIONS: The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. KEY POINTS: * T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly i

Purpose: The main objective of this preliminary analysis of the IMaging PAtients for Cancer drug selecTion (IMPACT)-renal cell cancer (RCC) study is to evaluate the lesion detection of baseline contrast-enhanced CT, [89Zr]Zr-DFO-girentuximab-PET/CT and [18F]FDG-PET/CT in detecting ccRCC lesions in patients with a good or intermediate prognosis metastatic clear cell renal cell carcinoma (mccRCC) according to the International Metastatic Database Consortium (IMDC) risk model. Methods: Between February 2015 and March 2018, 42 newly diagnosed mccRCC patients with good or intermediate prognosis, eligible for watchful waiting, were included. Patients underwent CT, [89Zr]Zr-DFO-girentuximab-PET/CT and [18F]FDG-PET/CT at baseline. Scans were independently reviewed and lesions of ≥10 mm and lymph nodes of ≥15 mm at CT were analyzed. For lesions with [89Zr]Zr-DFO-girentuximab or [18F]FDG-uptake visually exceeding background uptake, maximum standardized uptake values (SUVmax) were measured. Results: A total of 449 lesions were detected by ≥1 modality (median per patient: 7; ICR 4.25–12.75) of which 42% were in lung, 22% in lymph nodes and 10% in bone. Combined [89Zr]Zr-DFO-girentuximab-PET/CT and CT detected more lesions than CT alone: 91% (95%CI: 87–94) versus 56% (95%CI: 50–62, p = 0.001), respectively, and more than CT and [18F]FDG-PET/CT combined (84% (95%CI:79–88, p < 0.005). Both PET/CTs detected more bone and soft tissue lesions compared to CT alone. Conclusions: The addition of [89Zr]Zr-DFO-girentuximab-PET/CT and [18F]FDG-PET/CT to CT increases lesion detection compared to CT alone in newly diagnosed good and intermediate prognosis mccRCC patients eligible for watchful waiting.

Contains fulltext : 208199.pdf (publisher's version ) (Open Access), BACKGROUND: Surgery is the cornerstone of treatment of symptomatic cartilaginous neoplasms. We previously studied the application of radiofrequency ablation of atypical cartilaginous tumours in the long bones. The purpose of the present study was to investigate the additional effect of placing multiple needles and a longer procedure duration on the proportion of completely ablated tumours. Post-ablation MRI findings and the occurrence of complications were also assessed. METHODS: We prospectively included 24 patients with atypical cartilaginous tumours in the long bones. Patients underwent CT-guided radiofrequency ablation followed by curettage with adjuvant phenolisation 3 months later, retrieving material assessed for viable tumour. Before curettage, gadolinium-enhanced MRI was performed to check for residual tumour. The occurrence of complications was noted. RESULTS: Complete tumour ablation was achieved in 17 out of 24 patients (71%). Complete ablation was achieved in 5 of the 6 cases (83%) when multiple needles were used in tumours >/=30 mm. There was incomplete ablation in 8% of patients. Post-ablation gadolinium-enhanced MRI findings agreed with the histological results in 17 out of 23 cases and there was a negative predictive value of 83%. One patient suffered a fracture after radiofrequency ablation. CONCLUSION: Radiofrequency ablation could be an alternative to curettage when treating atypical cartilaginous tumours in the long bones. It was shown that multiple needle placement in addition to longer duration of the ablation procedure is an effective measure in achieving complete ablation in tumours >/=30 mm. Gadolinium-enhanced MRI has a negative predictive value of 83% and could guide post-ablation follow-up.