scholarly journals Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

2020 ◽  
Author(s):  
Constantinos Zamboglou ◽  
Alisa S. Bettermann ◽  
Christian Gratzke ◽  
Michael Mix ◽  
Juri Ruf ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Image Interpretation ◽  
Receiver Operating Curve ◽  
Training Cohort ◽  
Primary Prostate Cancer ◽  
Psma Pet ◽  
Number Of Patients ◽  
Clinically Significant

Abstract Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

OC-0208: Comparison between 68Ga-PSMA-PET/CT and mpMRI for radiotherapy planning in primary prostate cancer

2020 ◽  
Vol 152 ◽  
pp. S104
Author(s):  
S. Spohn ◽  
C. Jaegle ◽  
A.S. Bettermann ◽  
S. Kiefer ◽  
C.A. Jilg ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiotherapy Planning ◽  
Primary Prostate Cancer ◽  
Psma Pet ◽  
Pet Ct

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

Ultrasonic Adaptive Sound Speed Estimation for the Diagnosis and Quantification of Hepatic Steatosis: A Pilot Study

2018 ◽  
Vol 40 (06) ◽  
pp. 722-733 ◽  
Author(s):  
Marco Dioguardi Burgio ◽  
Marion Imbault ◽  
Maxime Ronot ◽  
Alex Faccinetto ◽  
Bernard E. Van Beers ◽  
...  
Keyword(s):  
Hepatic Steatosis ◽  
Sound Speed ◽  
Validation Cohort ◽  
Liver Steatosis ◽  
Receiver Operating Curve ◽  
Coefficient Of Determination ◽  
Proton Density ◽  
Speed Estimation ◽  
Training Cohort ◽  
Receiver Operating Curve Analysis

Abstract Purpose To evaluate the ability of a new ultrasound (US) method based on sound speed estimation (SSE) with respect to the detection, quantification, and grading of hepatic steatosis using magnetic resonance (MR) proton density fat fraction (PDFF) as the reference standard and to calculate one US fat index based on the patient’s SSE. Materials and Methods This study received local IRB approval. Written informed consent was obtained from patients. We consecutively included N = 50 patients as the training cohort and a further N = 50 as the validation cohort who underwent both SSE and abdominal MR. Hepatic steatosis was classified according to MR-PDFF cutoffs as: S0 ≤ 6.5 %, S1 6.5 to 16.5 %, S2 16.5 to 22 %, S3 ≥ 22 %. Receiver operating curve analysis was performed to evaluate the diagnostic performance of SSE in the diagnosis of steatosis (S1–S3). Based on the optimal data fit derived from our study, we proposed a correspondence between the MR-PDFF and a US fat index. Coefficient of determination R2 was used to evaluate fit quality and was considered robust when R2 > 0.6. Results The training and validation cohorts presented mean SSE values of 1.570 ± 0.026 and 1.568 ± 0.023 mm/µs for S0 and 1.521 ± 0.031 and 1.514 ± 0.019 mm/µs for S1–S3 (p < 0.01) patients, respectively. An SSE threshold of ≤ 1.537 mm/µs had a sensitivity of 80 % and a specificity of 85.7 % in the diagnosis of steatosis (S1-S3) in the training cohort. Robust correspondence between MR-PDFF and the US fat index was found both for the training (R2 = 0.73) and the validation cohort (R2 = 0.76). Conclusion SSE can be used to detect, quantify and grade liver steatosis and to calculate a US fat index.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

Comparison of 68Ga-PSMA PET CT and multi-parametric MRI in the evaluation of tumor extension of primary prostate cancer

2019 ◽  
Vol 18 (1) ◽  
pp. e214
Author(s):  
X. Qiu ◽  
M. Chen ◽  
Z. Qing ◽  
J. Gao ◽  
C. Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Extension ◽  
Primary Prostate Cancer ◽  
Psma Pet ◽  
Pet Ct

A urine exosomal circRNA classifier for detection of high-grade prostate cancer at initial biopsy: A multicenter, retrospective study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5522-5522
Author(s):  
Liaoyuan Li ◽  
Wen Tao ◽  
Yadi He ◽  
Tao He ◽  
Qing Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Circular Rna ◽  
High Grade ◽  
Training Cohort ◽  
Potential Biomarker ◽  
Initial Biopsy

5522 Background: The low specificity of prostate-specific antigen (PSA) has resulted in the overdiagnosis and overtreatment of clinically indolent prostate cancer (PCa). We aimed to identify a urine exosomal circular RNA (circRNA) classifier that could detect high-grade (Gleason score [GS]7 or greater) PCa. Methods: We did a three-stage study that enrolled eligible participants, including PCa-free men, 45 years or older, scheduled for an initial prostate biopsy due to suspicious digital rectal examination findings and/or PSA levels (limit range, 2.0-20.0 ng/mL), from four hospitals in China. We used RNA sequencing and digital droplet polymerase chain reaction to identify 18 candidate urine exosomal circRNAs that were increased in 11 patients with high-grade PCa compared with 11 case-matched patients with benign prostatic hyperplasia. Using a training cohort of eligible participants, we built a urine exosomal circRNA classifier (Ccirc) to detect high-grade PCa. We then evaluated the classifier in discrimination of GS7 or greater from GS6 and benign disease on initial biopsy in two independent cohorts. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared Ccirc with standard of care (SOC) (ie, PSA level, age, race, and family history). Results: Between June 1, 2016, and July 31, 2019, we recruited 356 participants to the training cohort, and 442 and 325 participants to the two independent validation cohorts. We identified a Ccirc containing five differentially expressed circRNAs (circ_0049335, circ_0056536, circ_0004028, circ_0008475, and circ_0126027) that could detect high-grade PCa. Ccirc showed higher accuracy than SOC to distinguish individuals with high-grade PCa from controls in both the training cohort and the validation cohorts. (AUC 0.831 [95% CI 0.765-0.883] vs 0.724 [0.705-0.852], P = 0.032 in the training cohort; 0.823 [0.762-0.871] vs 0.706 [0.649-0.762], P = 0.007 in validation cohort 1; and 0.878 [0.802-0.943] vs 0.785 [0.701-0.890], P = 0.021 for validation cohort 2). In all three cohorts, Ccirc had higher sensitivity (range 71.6-87.2%) and specificity (82.3-90.7%) than did SOC (sensitivity, 42.3-68.2%; specificity, 40.1-62.3%) to detect high-grade PCa. Using a predefined cut point, 202 of 767 (26.3%) biopsies would have been avoided, missing only 6% of patients with dominant pattern 4 high-risk GS 7 disease. Conclusions: Ccirc is a potential biomarker for high-grade PCa among suspicious men.

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