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1.
Pancreas ; 53(2): e199-e204, 2024 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-38127849

RESUMO

OBJECTIVES: Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. MATERIALS AND METHODS: Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. RESULTS: Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. CONCLUSIONS: Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.


Assuntos
Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Inteligência Artificial , Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/tratamento farmacológico , Carcinoma Ductal Pancreático/cirurgia , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/cirurgia , Prognóstico , Aprendizado de Máquina , Estudos Retrospectivos
2.
Sci Rep ; 11(1): 6541, 2021 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-33753854

RESUMO

The collagen gel droplet-embedded drug sensitivity test (CD-DST) was revealed to be useful for predicting the effect of S-1 adjuvant chemotherapy for pancreatic ductal adenocarcinoma (PDAC). However, collection of an adequate number of PDAC cells is difficult due to the surrounding fibroblasts. Thus, the aim of this study was to discover novel biomarkers to predict chemosensitivity based on the CD-DST results. Proteomics analysis was performed using liquid chromatography tandem mass spectrometry (LC-MS/MS). Candidate proteins were validated in patients with 5-FU CD-DST results via immunohistochemistry (IHC). The relationships between the candidate proteins and the effect of the adjuvant S-1 were investigated via IHC. Among the 2696 proteins extracted by LC-MS/MS, C1TC and SAHH could accurately predict the CD-DST results. Recurrence-free survival (RFS) was significantly improved in the IHC-positive group compared with the IHC-negative group in both factors. The negative group did not show a significant difference from the group that did not receive S-1. The double-positive group was associated with significantly prolonged RFS compared to the no adjuvant chemotherapy group. C1TC and SAHH have been shown to be useful biomarkers for predicting 5-FU sensitivity as a substitute for the CD-DST in adjuvant chemotherapy for PDAC.


Assuntos
Adenocarcinoma/tratamento farmacológico , Adenosil-Homocisteinase/genética , Carcinoma Ductal Pancreático/tratamento farmacológico , Resistencia a Medicamentos Antineoplásicos/genética , Tensinas/genética , Adenocarcinoma/genética , Adenocarcinoma/patologia , Idoso , Idoso de 80 Anos ou mais , Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Biomarcadores Farmacológicos/metabolismo , Biomarcadores Tumorais/genética , Carcinoma Ductal Pancreático/genética , Carcinoma Ductal Pancreático/patologia , Cromatografia Líquida , Colágeno/química , Colágeno/efeitos dos fármacos , Intervalo Livre de Doença , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Ensaios de Seleção de Medicamentos Antitumorais , Feminino , Fluoruracila/administração & dosagem , Fluoruracila/efeitos adversos , Humanos , Masculino , Pessoa de Meia-Idade , Proteínas de Neoplasias/genética , Recidiva Local de Neoplasia/tratamento farmacológico , Recidiva Local de Neoplasia/genética , Recidiva Local de Neoplasia/patologia , Proteômica , Espectrometria de Massas em Tandem
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