RESUMO
BACKGROUND AND AIMS: EUS-guided radiofrequency ablation (EUS-RFA) has emerged as an alternative for the local treatment of unresectable pancreatic ductal adenocarcinoma (PDAC). We assessed the feasibility and safety of EUS-RFA in patients with unresectable PDAC. METHODS: This study followed an historic cohort compounded by locally advanced (LA-) and metastatic (m)PDAC-naïve patients who underwent EUS-RFA between October 2019 and March 2022. EUS-RFA was performed with a 19-gauge needle electrode with a 10-mm active tip for energy delivery. Study primary endpoints were feasibility, safety, and clinical follow-up, whereas secondary endpoints were performance status (PS), local control, and overall survival (OS). RESULTS: Twenty-six patients were selected: 15 with locally advanced pancreatic duct adenocarcinoma (LA-PDAC) and 11 with metastatic pancreatic duct adenocarcinoma (mPDAC). Technical success was achieved in all patients with no major adverse events. Six months after EUS-RFA, OS was seen in 11 of 26 patients (42.3%), with significant PS improvement (P = .03). Local control was achieved, with tumor reduction from 39.5 mm to 26 mm (P = .04). A post-treatment hypodense necrotic area was observed at the 6-month follow-up in 11 of 11 patients who were still alive. Metastatic disease was a significant factor for worsening OS (hazard ratio, 5.021; 95% confidence interval, 1.589-15.87; P = .004). CONCLUSIONS: EUS-RFA for the treatment of pancreatic adenocarcinoma is a minimally invasive and safe technique that may have an important role as targeted therapy for local treatment of unresectable cases and as an alternative for poor surgical candidates. Also, RFA may play a role in downstaging cancer with a potential increase in OS in nonmetastatic cases. Large prospective cohorts are required to evaluate this technique in clinical practice.
Assuntos
Carcinoma Ductal Pancreático , Endossonografia , Neoplasias Pancreáticas , Ablação por Radiofrequência , Humanos , Neoplasias Pancreáticas/cirurgia , Neoplasias Pancreáticas/patologia , Neoplasias Pancreáticas/diagnóstico por imagem , Masculino , Feminino , Carcinoma Ductal Pancreático/cirurgia , Carcinoma Ductal Pancreático/patologia , Carcinoma Ductal Pancreático/diagnóstico por imagem , Idoso , Endossonografia/métodos , Pessoa de Meia-Idade , Ablação por Radiofrequência/métodos , Estudos de Coortes , Estudos de Viabilidade , Idoso de 80 Anos ou mais , Ultrassonografia de Intervenção , Estudos Retrospectivos , Resultado do TratamentoRESUMO
BACKGROUND: We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and to clinically validate the model through comparisons with DSOC expert and nonexpert endoscopists. METHODS: In this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). Lesions were classified into neoplastic and non-neoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and 12-month follow-up outcomes. RESULTS: In stage I, CNN2 achieved a mean average precision of 0.88, an intersection over the union value of 83.24â%, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. Half of cases (50â%) had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis, with a 90.5â% sensitivity, 68.2â% specificity, and 74.0â% and 87.8â% positive and negative predictive values, respectively. The CNN2 model outperformed nonexpert #2 (area under the receiver operating characteristic curve [AUC]-CRM 0.657 vs. AUC-CNN2 0.794, Pâ<â0.05; AUC-Mendoza 0.582 vs. AUC-CNN2 0.794, Pâ<â0.05), nonexpert #4 (AUC-CRM 0.683 vs. AUC-CNN2 0.791, Pâ<â0.05), and expert #4 (AUC-CRM 0.755 vs. AUC-CNN2 0.848, Pâ<â0.05; AUC-Mendoza 0.753 vs. AUC-CNN2 0.848, Pâ<â0.05). CONCLUSIONS: The proposed CNN model distinguished neoplastic bile duct lesions with good accuracy and outperformed two nonexpert and one expert endoscopist.