Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done using MRI. Nevertheless, l--methyl-C-methionine (C-MET) PET holds great potential in the characterization of gliomas. The aim of this study was to establish machine-learning-driven survival models for glioma built on in vivo C-MET PET characteristics, ex vivo characteristics, and patient characteristics. The study included 70 patients with a treatment-naïve glioma that was C-MET-positive and had histopathology-derived ex vivo feature extraction, such as World Health Organization 2007 tumor grade, histology, and isocitrate dehydrogenase 1 R132H mutational status. TheC-MET-positive primary tumors were delineated semiautomatically on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying 5 different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body mass index, Karnofsky score), were merged to characterize the tumors. Machine-learning approaches were used to identify relevant in vivo, ex vivo, and patient features and their relative weights for predicting 36-mo survival. The resulting feature weights were used to establish 3 predictive models per binning configuration: one model based on a combination of in vivo, ex vivo, and clinical patient information (M36); another based on in vivo and patient information only (M36); and a third based on in vivo information only (M36). In addition, a binning-independent model based on ex vivo and patient information only (M36) was created. The established models were validated in a Monte Carlo cross-validation scheme. The most prominent machine-learning-selected and -weighted features were patient-based and ex vivo-based, followed by in vivo-based. The highest areas under the curve for our models as revealed by the Monte Carlo cross-validation were 0.9 for M36, 0.87 for M36, 0.77 for M36, and 0.72 for M36 Prediction of survival in amino acid PET-positive glioma patients was highly accurate using computer-supported predictive models based on in vivo, ex vivo, and patient features.
Objectives Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. Methods This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network–derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). Results Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18–85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8–89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). Conclusion The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies. Key Points • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.
To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative. Methods: Using predefined criteria, a systematic review was performed. Studies investigating the use of supplemental CE-MRI in the setting of mammographically suspicious calcifications undergoing stereotactic biopsy and published between 2000 and 2020 were eligible. Two reviewers extracted study characteristics and true positives (TP), false positives, true negatives and false negatives (FN). Specificity, in this setting equaling the number of avoidable biopsies and FN rates were calculated. The maximum pre-test probability at which post-test probabilities of a negative CE-MRI met with BI-RADS benchmarks was determined by a Fagan nomogram. Random-effects models, I 2-statistics, Deek's funnel plot testing and meta-regression were employed. P-values <0.05 were considered significant. Results: Thirteen studies investigating 1414 lesions with a cancer prevalence of 43.6% (range: 22.7 e66.9%) were included. No publication bias was found (P ¼ 0.91). CE-MRI performed better in pure microcalcification studies compared to those also including associate findings (P < 0.001). In the first group, the pooled rate of avoidable biopsies was 80.6% (95%-CI: 64.6e90.5%) while the overall and invasive cancer FN rates were 3.7% (95%-CI: 1.2e6.2%) and 1.6% (95%-CI 0e3.6%), respectively. Up to a pretest probability of 22%, the post-test probability did not exceed 2%. Conclusion: A negative supplementary CE-MRI could potentially avoid 80.6% of unnecessary stereotactic biopsies in BI-RADS 4 microcalcifications at a cost of 3.7% missed breast cancers, 1.6% invasive. BI-RADS benchmarks for downgrading mammographic calcifications would be met up to a pretest probability of 22%.
To investigate the effects of a rectal preparation regimen, that consisted of a rectal cleansing enema and an endorectal gel filling protocol, on prostate imaging quality (PI-QUAL). Methods: Multiparametric MRI (mpMRI) was performed in 150 consecutive patients divided into two groups of 75 patients. One group received a rectal preparation with a cleansing enema and endorectal gel filling (median age 65.3 years, median PSA level 6 ng/ml). The other patient group did not receive such a preparation (median age 64 years, median PSA level 6 ng/ml). Two uroradiologists independently rated general image quality and lesion visibility on diffusion-weighted imaging (DWI), T2-weighted (T2w), and dynamic contrast-enhanced (DCE) images using a five-point ordinal scale. In addition, two uroradiologists assigned PI-QUAL scores, using the dedicated scoring sheet. Data sets were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/ area under the curve (AUC) analysis. Results: VGC revealed significantly better general image quality for DWI (AUC R1 0.708 (0.628-0.779 CI, p < 0.001; AUC R2 0.687 (0.606-0.760 CI, p < 0.001) and lesion visibility for both readers (AUC R1 0.729 (0.607-0.831 CI, p < 0.001); AUC R2 0.714 (0.590-0.818CI, p < 0.001) in the preparation group. For T2w imaging, rectal preparation resulted in significantly better lesion visibility for both readers (R1 0.663 (0.537-0.774 CI, p = 0.014; R2 0.663 (0.537-0.774 CI, p = 0.014)). Averaged PI-QUAL scores were significantly improved with rectal preparation (AUC R3/R4 0.667, CI 0.581-0.754, p < 0.001). Conclusion: Rectal preparation significantly improved prostate imaging quality (PI-QUAL) and lesion visibility. Hence, a rectal preparation regimen consisting of a rectal cleansing enema and an endorectal gel filling could be considered.
Purpose To assess the impact of frailty on compliance of standard therapy, complication, rate and survival in patients with gynecological malignancy aged 80 years and older. Methods In total, 83 women with gynecological malignancy (vulva, endometrial, ovarian or cervical cancer) who underwent primary treatment between 2007 and 2017 were retrospectively analyzed. Frailty index was calculated and its association with compliance of standard treatment, peri- and postoperative mortality and morbidity, and survival was evaluated. Results Frailty was observed in 24.1% of cases. Both frail and non-frail patients were able to receive standard therapy in most cases − 75.0% and 85.7%, respectively (p = 0.27). Frail patients did not show an increased postoperative complication rate. Frail patients had shorter 3 years overall survival rates (28%) when compared to non-frail patients (55%) (p = 0.02). In multivariable analysis high frailty index (Hazard Ratio [HR] 12.15 [1.39–106.05], p = 0.02) and advanced tumor stage (HR 1.33 [1.00–1.76], p = 0.05) were associated with poor overall survival, but not age, histologic grading, performance status, and compliance of standard therapy. Conclusion Majority of patients was able to receive standard therapy, as suggested by the tumor board, irrespective of age and frailty. Nonetheless, frailty is a common finding in patients with gynecological malignancy aged 80 years and older. Frail patients show shorter progression-free, and overall survival within this cohort.
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