An automatic classification method of testicular histopathology based on SC-YOLO framework.

The pathological diagnosis and treatment of azoospermia depend on precise identification of spermatogenic cells. Traditional methods are time-consuming and highly subjective due to complexity of Johnsen score, posing challenges for accurately diagnosing azoospermia. Here, we introduce a novel SC-YOLO framework for automating the classification of spermatogenic cells that integrates S3Ghost module, CoordAtt module and DCNv2 module, effectively capturing texture and shape features of spermatogenic cells while reducing model parameters. Furthermore, we propose a simplified Johnsen score criteria to expedite the diagnostic process. Our SC-YOLO framework presents the higher efficiency and accuracy of deep learning technology in spermatogenic cell recognition. Future research endeavors will focus on optimizing the model's performance and exploring its potential for clinical applications.

YOLO framework was optimized as SC-YOLO and applied to shape detection for automatic classification of spermatogenic cells in testicular pathology images.The SC-YOLO framework has high accuracy in identifying results from binomial distributions, with or without haploid germ cells.The SC-YOLO framework also has good performance in the identification of other types of pathological sections.

Efficacy and safety of FOLFIRI and biotherapy versus FOLFIRI alone for metastatic colorectal cancer patients: A meta-analysis.

BACKGROUND: Previous randomized controlled trials (RCTs) and meta-analyses have demonstrated the useless of FOLFIRI alone for previously treated patients with metastatic colorectal cancer (mCRC). The role of FOLFIRI regimen combined with biological therapy is unknown. The purpose of this meta-analysis is to evaluate the efficacy and safety of combining biological therapy with chemotherapy in previously treated patients with mCRC. METHODS: MEDLINE, EMBASE, Web of Science, Cochrane library, and ClinicalTrials.gov were searched. Eligible studies were RCTs that evaluated the efficacy and safety of the FOLFIRI regimen with or without biological therapy for previously treated patients with mCRC. The hazard ratio (HR) or risk ratio (RR) with 95% confidence interval was estimated. The Chi-squared and I-squared tests were used to assess the statistical heterogeneity. RESULTS: The literature search identified 7 RCTs that met the inclusion criteria for the meta-analysis, and 3680 patients with mCRC were included. The meta-analysis showed that combined therapy was associated with a significant improved progression-free survival (PFS) (HR = 0.78, 95% CI = 0.72-0.85, P < .001), overall survival (OS) (HR = 0.84, 95% CI = 0.77-0.92, P < .001), and overall response rate (ORR) (RR = 1.70, 95% CI = 1.25-2.31, P = .001). Sensitivity analysis suggested that combined therapy versus FOLFIRI alone might increase the risk of Grade 3/4 AEs. CONCLUSION: The addition of biological therapy to the FOLFIRI regimen improved the PFS, OS, and ORR compared with FOLFIRI alone for previously treated patients with mCRC. Long-term survival outcomes are warranted.