Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI.

Background: Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine. Objective: The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group >1,000 men. Methods: PHID values were used from available patient data with PSA, free PSA, and [−2]pro­PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA). Results: PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; p always <0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; p = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm³, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, p = 0.014). Conclusions: Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. To explore how well the tri-compartmental RSIperforms in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. Prospective. 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion , hindered diffusion , and free diffusion ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. The , SUVmax were significantly higher, and , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( ) > AUC ( ) > AUC (ADC) > AUC ( ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. RSI-derived parameters ( , ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. Stage 2.

Peripheral zone prostate-specific antigen density: an effective parameter for prostate cancer prediction in men receiving 5α-reductase inhibitors

The objective of our study was to determine the diagnostic accuracy and interobserver agreement of 1.5-T prostatic MRI for per-sextant tumor localization and staging of prostate cancer as compared with whole-mount step section histopathology. Combined endorectal-pelvic phased-array prostatic MRI scans obtained at 1.5 T of 106 patients with biopsy-proven prostate cancer who had undergone radical prostatectomy with whole-mount step section histopathology within 28 days of MRI were retrospectively analyzed by three independent abdominal radiologists (reviewers 1, 2, and 3). Sextants of the prostate (right and left base, middle, and apex) were evaluated for the presence of prostate cancer and extracapsular extension (ECE) using a 5-point confidence scale. Data were statistically analyzed using receiver operating characteristic (ROC) analysis. Interobserver variability was assessed by kappa statistics. For calculation of sensitivity and specificity, data from the 5-point confidence scale were dichotomized into negative (score of 1-3) or positive (score of 4 or 5) findings. Forty-one patients had ECE (tumor stage T3), and 65 patients had organ-confined disease (stage T2). Of 636 prostatic sextants, 417 were positive for prostate cancer and 135 were positive for ECE at histopathology. For prostate cancer localization, ROC analysis yielded area under the ROC curve (AUC) values ranging from 0.776 +/- 0.023 (SD) to 0.832 +/- 0.027. For the detection of ECE, the AUC values ranged from 0.740 +/- 0.054 to 0.812 +/- 0.045. Interobserver agreement (kappa) ranged from 0.49 to 0.60 for prostate cancer localization and from 0.59 to 0.67 for the detection of ECE. Using the sextant framework, independent observers reach similar accuracy with moderate to substantial agreement for the localization of prostate cancer and ECE by means of MRI of the prostate.

Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

Актуальность. Новые возможности улучшения функции внешнего дыхания (ФВД) связаны с применением высокочастотной осцилляции грудной клетки (ВЧОГК), которая позволяет восстановить дренаж бронхиального дерева и оптимизировать легочную вентиляцию. Цель исследования — изучение и оценка ФВД у детей с внебольничной пневмонией (ВП). Материалы и методы. Проведено обследование 107 детей (основная группа (ОГ) — 55 человек и контрольная группа (КГ) — 52 человека) в возрасте 6–17 лет с ВП с острым и неосложненным течением. Дети ОГ получали базисную терапию (БТ) с проведением процедур ВЧОГК, тогда как дети КГ получали исключительно БТ. Исследовали ФВД в динамике лечения, применяя спирометрию. Результаты. В начале терапии ВП показатели ФВД в исследуемых группах имели несущественные различия. При анализе параметров ФВД у детей ОГ на 10-й день БТ отмечено повышение объема форсированного выдоха за 1-ю секунду (ОФВ1) (88,36 ± 1,55 %, p = 0,02), жизненной емкости легких (ЖЕЛ) (88,18 ± 1,53 %, p = 0,02), форсированной жизненной емкости легких (ФЖЕЛ) (86,77 ± 1,37 %, p = 0,03), максимальной объемной скорости воздуха на уровне выдоха 25 % (МОШ25) (90,10 ± 2,99 %, p = 0,02) и максимальной вентиляции легких (МВЛ) (88,31 ± 1,70 %, p = 0,04). Сравнивая показатели ФВД детей КГ, выявили менее выраженную динамику, в частности, ОФВ1 (81,65 ± 2,44 %), ЖЕЛ (82,95 ± 2,56), ФЖЕЛ (80,85 ± 2,09), МОШ25 (82,63 ± 3,08 %) и МВЛ (85,65 ± 1,99 %). При проведении ROC-анализа динамика восстановления ФВД у детей ОГ более заметна за счет улучшения МВЛ, о чем свидетельствует самая большая площадь под кривой ROC (ППК) — 0,99, ОФВ1 — ППК 0,94 и ОФВ1/ФЖЕЛ — ППК 0,94. У детей КГ была менее выражена динамика исследуемых параметров ФВД, что подтверждается МВЛ — ППК 0,63, ОФВ1 — ППК 0,79 и ОФВ1/ФЖЕЛ — ППК 0,89. Выводы. Для улучшения вентиляционной функции легких у детей с ВП необходимо проводить процедуры бронходренажа, используя ВЧОГК в составе БТ, о чем свидетельствуют полученные положительные результаты спирометрии.

This research compares alternative performance metrics to those more commonly used in target detection system performance evaluation. The alternative performance metrics examined here include the Fisher ratio, a modification of the Dice similarity coefficient, and the Youden index. These metrics are compared to metrics that have been previously introduced for such target detection system performance evaluation: the receiver operating characteristic (ROC) curve (and the related summary area under the ROC curve (AUC) value), and the confidence error generation (CEG) curve (and the related summary root square deviation (RSD) value). The ROC curve is a discrimination metric that measures the ability of a target detection system to distinguish between target and non-target. The CEG curve quantifies detection system knowledge of its own performance. An approach is presented that combines such metrics; this combination may be dynamically adjusted and updated based on current and future evaluation requirements for particular target detection systems.

AIMS Around half of patients with brain metastases respond to immune checkpoint inhibition and these treatments have significant side effects, highlighting the critical need for treatment response biomarkers. Diffusion Tensor Imaging (DTI) represents a rapidly obtained MRI sequence that has been shown to discriminate high from low peritumoural immune infiltration and could therefore help personalise treatment approaches. METHODS This prospective single-centre biomarker study (CRUK funded) recruited adult patients with intracranial melanoma metastases eligible for immunotherapy. All patients received standard ipilimumab/nivolumab com- bination immunotherapy. We assessed pre-treatment DTI and 6-week follow-up scans using RANO and RE- CIST criteria. Peritumoral fractional anisotropy (FA) values from the DTI scans were measured blind to clin- ical outcomes using both manual (two raters; one being a neuroradiologist with 10+ years experience) and semi-automated techniques. We performed receiver operating characteristic (ROC) curve analysis to assess the discriminative performance of FA measurements in predicting both intracranial and extracranial response, calculating area under the ROC curve (AUC) values for both measurement techniques. RESULTS Eight patients with 18 brain metastases were included. Median overall survival was 13.1 months (95% CI: 1.6-24.6), with median intracranial progression-free survival of 12.9 months (95% CI: 7.2-18.6) and median ex- tracranial progression-free survival of 14.3 months (95% CI: 11.7-16.9). Both manual and semi-automated FA measurements demonstrated excellent predictive value for brain metastasis response (AUC=0.85 and 0.825, re- spectively), while showing poor discrimination for extracranial response (AUC=0.333 and 0.556, respectively). There was discordance between automated and manual FA readings but excellent inter-rater agreement for manual recording of FA. CONCLUSION This study demonstrates that DTI-derived FA measurements show promise as a predictive biomarker for im- munotherapy response in brain metastases, providing a potentially useful tool for optimizing patient selection. Further refinement of the semi-automated technique for measurement (e.g. via a plugin for PACS) and valida- tion in a larger cohort with diverse primary tumour types is needed

Cognitive reserve in patients with mood disorders: Validation study of the Chinese version of the cognitive reserve assessment scale in health

Objective To explore the value of quantitative T2 weighted magnetic resonance imaging (T2WI) and diffusion tensor imaging (DTI) parameters for predicting functional outcomes after surgery for cervical spondylotic myelopathy (CSM). Methods One hundred and forty CSM patients received T2WI and DTI before surgery at Tianjin Hospital between April 2014 and April 2016. They were then given systematic rehabilitation treatment after the surgery. The Japanese Orthopaedics Association (JOA) scoring system was applied to evaluate their neurological function before and after the surgery. According to the JOA recovery rate at 1-year follow-up, the patients were divided into a good recovery group (recovery rate≥50%) and a poor recovery group (recovery rate <50%). Maximum spinal cord compression (MSCC), signal change ratio (SCR), transverse area (TA), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were compared between the two groups. Receiver operating characteristics (ROC) curves were used to measure the prognostic ability and determine the best cut-off value for each variable. The independent predictors of a poor recovery were estimated using univariate and multivariate analysis. Results ROC analysis showed that the area under the ROC curve (AUC) of MSCC was 0.593. For SCR it was 0.682. For the TA it was 0.706. For the ADC it was 0.719 and for FA it was 0.749. The respective cut-off values were 44% compression, 1.561, 0.46 cm2, 1.339×10-3 mm2/s and 0.386. FA had the largest AUC, followed by ADC, TA, SCR and MSCC. MSCC and SCR had low discrimination power (AUC<0.7) in predicting a poor recovery, whereas TA, ADC and FA had moderate discrimination power (AUC 0.7-0.9). Logistic multivariate regression showed that a low JOA score, TA≤0.46 cm2 or FA≤0.386 were independent risk factors for a poor recovery. A predicting model built according to the results of the logistic regression analysis gave an AUC of 0.87, significantly better than that of the JOA score. With a cut-off value of 0.36, the sensitivity and specificity were 80% and 77% respectively. Conclusions Combining T2WI and DTI parameters with the JOA score may better predict the recovery of patients with CSM. The values can also provide references for making up rehabilitation plans. Key words: T2 weighted magnetic resonance imaging; Diffusion tensor imaging; Cervical vertebrae; Spinal cord compression; Prognosis

Background: Our feasibility study employed a novel genomic detection methodology that enriches 5-hydroxymethylcytosine (5hmC) loci in cell free DNA (cfDNA) from the plasma of cancer patients using click chemistry coupled with sequencing and machine learning based classification methods. These classification methods were developed to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 2 pancreatic datasets consisting of 41 and 53 cancer subjects (Set 1 and 2). In addition, a control cohort of 260 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design. Methods: Machine learning methods, applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples. Measures of predictive performance using 5-fold cross validation coupled with out-of-fold Area Under the Receiver Operating Characteristic Curve (AUROC) measures were employed. Gene sets selected as part of biomarker discovery were further analyzed for disease relevance using pathway analysis tools (GSEA, mSigDB). Results: 260 controls and 229 cancers from four disease types (breast, lung, pancreas and prostate) were analyzed; more than 60% of cancer patients had early stage disease (I or II). Predictive performance, employing AUROC measures, was established for breast (0.89), lung (0.84), pancreas (set 1 - 0.95 and 2 - 0.93) and prostate (0.83). The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients including tumors &amp;lt; 2 cm and these samples exhibited a higher prediction probability score. The prostate cancer cohort consisted of both indolent and aggressive disease sample and prediction performance was equally high for both (AUROC for indolent vs aggressive was 0.81 and 0.77, respectively). Conclusions: These findings suggest that 5hmC changes in cfDNA enable non-invasive detection of early stage breast, pancreatic, prostate, and lung cancers. Furthermore, 5hmC profiling in cfDNA may enable the prediction of clinically relevant features such as tumor size in breast adenocarcinoma or indolent disease in prostate cancer. Finally, this study identified a suite of 5hmC biomarkers that may be further validated in larger, and more diverse, patient cohorts. Citation Format: Anna Bergamaschi, Jeremy Ku, Yuhong Ning, Francois Collin, Chris Ellison, Tierney Phillips, Erin McCarthy, Wendy Wang, Michael Antoine, David Haan, Aaron Scott, Paul Lloyd, Gulfem Guler, Alan Ashworth, Stephen Quake, Samuel Levy. Epigenomic detection of multiple cancers in plasma derived cell free DNA [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 783.

Ratio of visceral fat area to body fat mass (VBR) is a superior predictor of coronary heart disease.

Maximizing area under ROC curve for biometric scores fusion

With the aging of the world population, the number of elderly patients with acute pancreatitis (AP) is increasing. The morbidity and mortality are significantly increased in elderly AP patients. This study aims to establish a model to predict the mortality of elderly patients with AP. The retrospective cohort study included 329 patients aged ≥65 with AP in the Medical Information Mart for Intensive Care-III (MIMIC-III) public database. The nomogram was used to visualize the prognostic model and the calibration curve to evaluate the stability and accuracy of the prediction model. Receiver operating characteristic (ROC) curves were drawn to show the area under the ROC curve (AUC) value which determine the predictive value. The Z test was utilized to compare the difference of AUCs. The independent risk factors for mortality in elderly patients with AP included age (P = .019), pulse oxygen saturation (SpO2) (P = .048), Sequential Organ Failure Assessment (SOFA) (P = .007), simplified acute physiological score II (SAPS II) (P = .013) and red cell distribution width (RDW) (P = .018). The AUC of the nomogram prognostic model based on the above 5 indicators predicting mortality in elderly patients with AP was 0.805, with a sensitivity of 65.2% and a specificity of 85.9%, and the calibration curve showed good performance. In addition, the AUCs of RDW, SOFA, and SAPS II alone were 0.618, 0.759, and 0.729, respectively. A nomogram prognostic model included age, SpO2, SOFA, SAPS II and RDW showed good performance in predicting the mortality of elderly patients with AP.