Accuracy of fine-needle aspiration of lymph nodes: A cancer center's experience.

INTRODUCTION: Lymph node fine needle aspiration (LN-FNA) is a minimally invasive method of evaluating lymphadenopathy. Nonetheless, its use is not widely accepted due to the lack of guidelines and a cytopathological categorisation that directly relates to management. We report our experience with LN FNA at a large Cancer Center in Latin America. METHODS: We retrospectively collected cytological cases of lymph node FNA from the department of pathology at AC Camargo Cancer Center performed over a 2-year period. Data extracted included LN location, age, sex and final cytological diagnosis. Patients that had undergone neoadjuvant chemotherapy and/or cases for which the surgery specimen location was not clearly reported were excluded. For those cases with surgical reports, risk of malignancy was calculated for each diagnostic category, along with overall performance of cytology. False positive cases were reviewed to assess any possible misinterpretation or sampling errors. RESULTS: A total of 1730 LN-FNA were distributed as follows: 62 (3.5%) non-diagnostic (ND); 1123 (64.9%) negative (NEG), 19 (1.1%) atypical (ATY), 53 (3.1%) suspicious for malignancy (SUS), and 473 (27.3%) positive (POS). Surgical reports were available for 560 cases (32.4%). Risk of malignancy (ROM) for each category was 33.3% for ND, 29.9% for NEG, 25% for ATY, 74.2% for SUS and 99.6% for POS. Overall sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were 78.5%, 99.4%, 70.2% and 99.6%, respectively. CONCLUSION: Lymph node FNA is a very specific and accurate exam, which is reliable in the detection of lymph node metastasis and other causes of lymphadenopathy.

Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer.

DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.