Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathologists in grading and diagnosing these tumors based on histopathological images. The appropriate treatment plan for the patient is determined based on the tumor grade. An artificial intelligence-based method is proposed to aid pathologists in the automated calculation and grading of the Ki-67 proliferation index. The proposed system first performs preprocessing to enhance image quality. Then, segmentation process is performed using the U-Net architecture, which is a deep learning algorithm, to separate the nuclei from the background. The identified nuclei are then evaluated as Ki-67 positive or negative based on basic color space information and other features. The Ki-67 proliferation index is then calculated, and the neuroendocrine tumor is graded accordingly. The proposed system's performance was evaluated on a dataset obtained from the Department of Pathology at Meram Faculty of Medicine Hospital, Necmettin Erbakan University. The results of the pathologist and the proposed system were compared, and the proposed system was found to have an accuracy of 95% in tumor grading when compared to the pathologist's report.

A score for predicting colchicine resistance at the time of diagnosis in familial Mediterranean fever: data from the TURPAID registry.

OBJECTIVES: Colchicine forms the mainstay of treatment in FMF. Approximately 5-10% of FMF patients are colchicine resistant and require anti-IL-1 drugs. We aimed to compare the characteristics of colchicine-resistant and colchicine-responsive patients and to develop a score for predicting colchicine resistance at the time of FMF diagnosis. METHODS: FMF patients (0-18 years) enrolled in the Turkish Paediatric Autoinflammatory Diseases (TURPAID) registry were included. The predictive score for colchicine resistance was developed by using univariate/multivariate regression and receiver operating characteristics analyses. RESULTS: A total of 3445 FMF patients [256 (7.4%) colchicine-resistant and 3189 colchicine-responsive) were included (female:male ratio 1.02; median age at diagnosis 67.4 months). Colchicine-resistant patients had longer, more frequent attacks and were younger at symptom onset and diagnosis (P < 0.05). Fever, erysipelas-like erythema, arthralgia, arthritis, myalgia, abdominal pain, diarrhoea, chest pain, comorbidities, parental consanguinity and homozygosity/compound heterozygosity for exon 10 MEFV mutations were significantly more prevalent among colchicine-resistant than colchicine-responsive patients (P < 0.05). Multivariate logistic regression analysis in the training cohort (n = 2684) showed that age at symptom onset, attack frequency, arthritis, chest pain and having two exon 10 mutations were the strongest predictors of colchicine resistance. The score including these items had a sensitivity of 81.3% and a specificity of 49.1%. In the validation cohort (n = 671), its sensitivity was 93.5% and specificity was 53.8%. CONCLUSION: We developed a clinician-friendly and practical predictive score that could help us identify FMF patients with a greater risk of colchicine resistance and tailor disease management individually at the time of diagnosis.

Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning.

PURPOSE: Rapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on testicular strips of prepubertal animal models. The purpose of this study is to detect and count the seminiferous tubules and SSPCs in newborn mouse testis sections using a DL method. METHODS: Testicular sections of the C57BL/6-type newborn mice were obtained and enumerated. Odd-numbered sections were stained with hematoxylin and eosin (H&E), and even-numbered sections were immune labeled (IL) with SSPC specific marker, SALL4. Seminiferous tubule and SSPC datasets were created using odd-numbered sections. SALL4-labeled sections were used as positive control. The YOLO object detection model based on DL was used to detect seminiferous tubules and stem cells. RESULTS: Test scores of the DL model in seminiferous tubules were obtained as 0.98 mAP, 0.93 precision, 0.96 recall, and 0.94 f1-score. The SSPC test scores were obtained as 0.88 mAP, 0.80 precision, 0.93 recall, and 0.82 f1-score. CONCLUSION: Seminiferous tubules and SSPCs on prepubertal testicles were detected with a high sensitivity by preventing human-induced errors. Thus, the first step was taken for a system that automates the detection and counting process of these cells in the infertility clinic.

Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study.

Growing pains (GP) are the most common cause of recurrent musculoskeletal pain in children. There are no diagnostic criteria for GP. We aimed at analyzing GP-related characteristics and assisting GP diagnosis by using machine learning (ML). Children with GP and diseased controls were enrolled between February and August 2019. ML models were developed by using tenfold cross-validation to classify GP patients. A total of 398 patients with GP (F/M:1.3; median age 102 months) and 254 patients with other diseases causing limb pain were enrolled. The pain was bilateral (86.2%), localized in the lower extremities (89.7%), nocturnal (74%), and led to awakening at night (60.8%) in most GP patients. History of arthritis, trauma, morning stiffness, limping, limitation of activities, and school abstinence were more prevalent among controls than in GP patients (p = 0.016 for trauma; p < 0.001 for others). The experiments with different ML models revealed that the Random Forest algorithm had the best performance with 0.98 accuracy, 0.99 sensitivity, and 0.97 specificity for GP diagnosis. This is the largest cohort study of children with GP and the first study that attempts to diagnose GP by using ML techniques. Our ML model may be used to facilitate diagnosing GP.

Anti-IL1 treatment in colchicine-resistant paediatric FMF patients: real life data from the HELIOS registry.

OBJECTIVES: FMF is a prototype of autoinflammatory diseases associated with excess IL1 production. Anti-IL1 treatments are the first-line alternatives in colchicine-resistant/intolerant FMF patients. We aimed to investigate the efficacy and safety of anti-IL1 treatment in paediatric FMF patients in our local [Hacettepe univErsity eLectronIc research fOrmS (HELIOS)] registry. METHODS: HELIOS is a web-based biologic drug registry for paediatric rheumatology patients. We have analysed the clinical features, disease activity parameters, treatment responses and safety outcomes in FMF patients treated with anti-IL1 agents. RESULTS: Forty paediatric FMF patients (34 continuous and six on-demand use) were included. Among the continuously treated group (61.7% female), the mean age at the start of colchicine was 5.55 (3.87) years. Age at onset of the anti-IL1 treatment was 11.47 (5.41) years with a mean follow-up duration of 3.87 (1.96) years. Apart from two, all patients had biallelic exon-10 mutations. We also gave anti-IL1 treatment on an on-demand basis in six patients. Anakinra was used as the first-line anti-IL1 treatment. During the last visit, six patients were treated with anakinra and 28 patients with canakinumab. Anti-IL1 treatment decreased the CRP levels and number and severity of the attacks. There were three hospitalizations reported due to mild infections. Eleven patients had local skin reactions, two patients had leucopenia with anakinra and one patient had thrombocytopenia with canakinumab. There was no malignancy or other severe adverse reactions. CONCLUSION: Anakinra and canakinumab are efficient and safe alternatives in colchicine-resistant or -intolerant paediatric FMF patients. We also, for the first time, report on-demand use of anti-IL1 in paediatric FMF patients.

Life sciences data and application integration with B-fabric.

In this demo paper, we sketch B-Fabric, an all-in-one solution for management of life sciences data. B-Fabric has two major purposes. First, it is a system for the integrated management of experimental data and scientific annotations. Second, it is a system infrastructure supporting on-the fly coupling of user applications, and thus serving as extensible platform for fast-paced, cutting-edge, collaborative research.