(126) ROLE OF TESTICULAR BIOPSY IN GUIDING MICRODISSECTION TESTICULAR SPERM EXTRACTION IN NON-OBSTRUCTIVE AZOOSPERMIA: A SINGLE-CENTER RETROSPECTIVE STUDY
Bibliographic record
Abstract
Abstract Introduction Non-obstructive azoospermia (NOA) is characterized by the absence of sperm in the ejaculate due to testicular failure. Testicular biopsy may play a crucial role in evaluating and managing NOA by providing valuable diagnostic information and guiding treatment decisions. Performing a testicular biopsy before microdissection testicular sperm extraction (mTESE) can determine the presence and degree of spermatogenesis, evaluate the feasibility of sperm retrieval, optimize the surgical approach, and contribute to patient counselling during a fertility workup. Minimal literature exists describing the utility of a testicular biopsy prior to an mTESE in the management of NOA. Objective The primary aim of this study is to evaluate the role of testicular biopsy in the assessment and management of NOA and to determine the necessity and willingness of patients to undergo further mTESE procedures based on the pathological findings. Methods This retrospective single-center study included adult males with NOA meeting criteria of two consecutive semen analyses demonstrating azoospermia, FSH levels >8 mIU/mL and normal karyotype/Y chromosome microdeletion (YCMD). Data was extracted from medical charts of patients seen by a single surgeon between September 2022 and June 2024. Demographic variables (age, BMI, FSH levels) and histopathology findings from testicular biopsies were collected ranging from Sertoli cell-only syndrome (SCO), hypospermatogenesis, and maturation arrest to normal testicular tissue. Linear regression analysis was performed to determine if histopathology types predicted patient decisions to undergo mTESE. Results This study examined the relationship between testicular histopathology and mTESE decisions in 19 patients (mean age 35.9 ± 5.5 years, BMI 26.9 ± 4.4 kg/m2, FSH 22.0 ± 15.8 mIU/mL). 68.4% had homogenous pathology, 26.3% had two pathologies, and 5.3% had three pathologies. Hypospermatogenesis was most common (46.2%), followed by SCO (38.5%) and maturation arrest (15.4%). Logistic regression analysis revealed that the odds of choosing mTESE increased by a factor of 5.33 (CI95%: 1.23-23.14) for each additional pathology observed. Patients with hypospermatogenesis were 3.08 times more likely to opt for mTESE compared to other pathologies (CI95%: 1.42-6.67). The probability of choosing mTESE increased from 42.86% for one pathology to 95.24% for three pathologies, with 30.77% of hypospermatogenesis, 19.23% of SCO, and 15.38% of maturation arrest patients opting for the procedure. Conclusions This single-center retrospective study underscores the pivotal role of testicular biopsy in the management of NOA. Histopathologic findings from testicular biopsies significantly influenced patients' decisions to proceed with a mTESE, with those diagnosed with hypospermatogenesis demonstrating a strong inclination towards pursuing mTESE. These findings highlight the clinical utility of histopathological assessment in guiding personalized treatment for NOA patients, optimizing the effectiveness of fertility interventions and enhancing patient counselling during fertility evaluations. Future prospective studies should further validate these findings across diverse patient populations to refine clinical algorithms for effective NOA management. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Dr. Premal Patel has been a consultant for Boston Scientific.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".