METHODS: The study included 18 patients with confirmed mediastinal lymphadenopathy who were admitted in Chest Department, Cairo University in the period from December 2019 to December 2020. All patients were subjected to flexible bronchoscopy with conventional transbronchial needle aspiration (C-TBNA) and transbronchial forceps biopsy (LN-TBFB) from the enlarged mediastinal lymph node in the same procedure.

RESULTS: we found the technique of LN-TBFB safe with no serious complications. We were able to reach a diagnosis in 7/7 (100%) cases of sarcoidosis, 6/7 (85.7%) cases of malignant lymph nodes. We had three cases where the histopathology showed hyperactive follicular hyperplasia, and a single case of tuberculous lymphadenitis. C-TBNA was diagnostic in 71.4% of sarcoidosis cases, 42.9% of malignant cases, but failed to diagnose the one patient with tuberculous lymphadenitis.

METHODS: This is a retrospective study over 8-year duration in which all the breast FNABs performed in our institution were recategorized in accordance to the IAC Yokohama reporting system. Kappa coefficient was used to evaluate the agreement between the proposed cytological category and corresponding histological diagnosis, with the level of significance set at 5%. Cyto-histopathological correlation and its diagnostic performance were also assessed.

RESULTS: A total of 1136 breast FNABs were analyzed, including 31 repeat FNABs. Of these, 521 (47.1%) cases had matched histopathological results. Respective ROM for each category was: "insufficient" 13.6%, "benign" 0.4%, "atypical" 25.0%, "suspicious" 85.7%, and "malignant" 100%. There was substantial agreement (κ=0.757) between cytology and histopathological results. Our data revealed a high-diagnostic specificity, sensitivity, positive and negative predictive value of 99.3% (95% CI: 97.6%-99.9%), 94.2% (95% CI: 87.9%-97.9%), 98.0% (95% CI: 92.5%-99.5%), 98.0% (95% CI: 96.1%-99.1%) respectively when both the "suspicious" and "malignant" cases were considered as positive tests, with area under the curve of 0.993.

MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.

RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).