The yield of cytology and histology obtained by endoscopic ultrasound-guided fine needle aspiration and biopsy needles in the diagnosis of pancreatic adenocarcinoma

The additive diagnostic value of cytology in fine needle biopsy of pancreatic adenocarcinoma: A tertiary center experience

OBJECTIVE

Endoscopic ultrasound guide fine needle biopsy (EUS-FNB) is the main diagnostic tool for pancreatic adenocarcinoma. In most instances, only histology is obtained via FNB, without sending cytological slides. The aim of our study was to assess the additive diagnostic yield of cytology performed through FNB.

METHODS

We conducted a retrospective study of all patients with histological diagnosis of pancreatic adenocarcinoma who were diagnosed by EUS-FNB.

RESULTS

Overall, 80 patients were included in the study period. The overall concordance between cytology and histology all FNB needles was 78.2%. Notably, cytological assessment improved the diagnostic yield for malignancy by 12.8%. The overall kappa coefficient correlation between histology and cytology was .501, 95% CI 0.361-0.641. However, the kappa correlation for suspicious of malignancy and malignant was excellent of .872, 95% CI 0.733-1, suggesting that cytology is crucial when histology is inconclusive. Further analysis showed that the Acquire and Sharkcore needles outperformed the Procore needle in term of concordance between cytology and histology (kappa correlation of .527, 95% CI 0.331-0.724, .515, 95% CI 0.265-0.764, and .297, 95% CI -0.051-0.646), respectively.

CONCLUSION

Performing cytology specimen when using FNB improves the diagnostic yield in pancreatic adenocarcinoma.

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.