Linguistic and Clinical Validation of the Tajik Acute Cystitis Symptom Score for Diagnosis and Patient-Reported Outcome in Acute Uncomplicated Cystitis.

Background and Objectives: Acute Cystitis Symptom Score (ACSS) is a self-reporting questionnaire for clinical diagnosis and follow-up of acute uncomplicated cystitis (AC) in women. The ACSS, originally developed in Uzbek and Russian, both considered original languages, is now available in several other languages. This study aimed to translate and validate the ACSS in the Tajik language. Material and Methods: Linguistic validation was carried out according to the Linguistic Validation Manual for Patient-Reported Outcomes Instruments guidelines. Clinical validation was performed by enrolling fifty-four Tajik-speaking women. All women included in this study were first interviewed about the understandability of all questions and statements in the final Tajik ACSS and were asked to fill in form A at the first visit (diagnostics) and form B at any follow-up visit (patient-reported outcome). Results: Thirty-three women, median (range) age of 35 (18-77), were diagnosed with AC (patient group), while twenty-one women, median (range) age of 34 (20-61) (p = 0.109), were enrolled as the control group without any other urological disease. For the diagnostics of AC, a summary score of the six typical symptoms ("Typical" domain) showed the best balance between sensitivity (0.73) and specificity (0.71) at 5 and above. Cronbach's alpha [95% CI] and split-half reliability [95%] were 0.82 [0.76; 0.98] and 0.84 [0.77; 0.87], respectively. At the follow-up visit, the patients reported a significant reduction in the "Typical" domain and an improvement in the "Quality of Life" domain. Conclusion: The Tajik ACSS showed good reliability and diagnostic values and may be used as a reliable tool for the diagnosis and patient-reported outcome in women with AC in clinical and epidemiological studies and for daily practice.

An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.

Evaluation of the draft guidelines proposed by EMA and FDA for the clinical diagnosis of acute uncomplicated cystitis in women.

PURPOSE: To reassess the diagnostic values of the "draft" guidelines for the clinical diagnosis of acute uncomplicated cystitis (AC), recently proposed by US Food and Drug Administration (FDA) and European Medicines Agency (EMA). METHODS: The data of 517 female respondents (patients with acute cystitis and controls) derived from the e-USQOLAT database were analyzed and used for the validation of proposed "draft" guidelines of FDA and EMA, compared to the Acute Cystitis Symptom Score (ACSS) questionnaire. The diagnostic values of the proposals concerning signs, symptoms and their severity were assessed and compared. RESULTS: The six "typical" symptoms of the ACSS were strongly associated with the diagnosis of AC. The number of positive "typical" symptoms differed significantly between patients and controls: median 5 (IQR 4-6) vs 1 (IQR 0-3) respectively. Scored severity of "typical" symptoms also differed significantly between groups of patients and controls: median (IQR) 10 (7-13) vs 1 (0-4), respectively. The best balance between sensitivity and specificity is shown by the ACSS cut-off value of 6 scores and more of the "Typical" domain, followed by an approach proposed by FDA and EMA, justifying ACSS to be used as a diagnostic criterion for the clinical diagnosis of AC. CONCLUSIONS: Not only the presence but also the severity of the symptoms is important for an accurate diagnosis of AC. The ACSS, even without urinalysis is at least as favourable as the draft diagnostic proposals by FDA and EMA. The ACSS can be recommended for epidemiological and interventional studies, and allows women to establish self-diagnosis of AC, making the ACSS also cost-effective for healthcare.