RESUMO
A 42-year-old woman was referred to our hospital for the treatment of pyloric stenosis. Esophagogastroduodenoscopy (EGD) and computed tomography (CT) revealed the presence of type 4 advanced gastric cancer with bladder metastasis and peritoneal dissemination. Biopsy specimen examination revealed poorly differentiated adenocarcinoma with signet ring cell carcinoma. Thus, two cycles of chemotherapy with 5-fluorouracil, leucovorin, and oxaliplatin were administered. Subsequently, the gastric lesion and bladder metastasis reduced as detected using EGD and CT. Consequently, she achieved adequate oral intake. After changing the regimen to tegafur/gimeracil/oteracil and oxaliplatin, she was discharged. Currently, she continues to receive chemotherapy.
Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Neoplasias Gástricas/diagnóstico , Neoplasias da Bexiga Urinária/secundário , Adulto , Feminino , Humanos , Leucovorina/uso terapêutico , Compostos Organoplatínicos/uso terapêutico , Neoplasias Gástricas/terapia , Bexiga Urinária/patologia , Neoplasias da Bexiga Urinária/terapiaRESUMO
A 61-year-old man was referred to the urology department in our hospital with gross hematuria and hydro nephrosis. Cystoscopy revealed a smooth mass lesion in his bladder, and a transurethral biopsy was performed. Signet ring cell carcinoma was found in the submucosa. Upper gastrointestinal endoscopy revealed an ulcerated lesion in his gastric body. Biopsy specimens obtained from the ulcerated lesion showed signet ring cell carcinoma. No other primary lesions were detected using colonoscopy, gallium scintigraphy, or computed tomography of the chest. He was ultimately diagnosed with advanced gastric cancer with bladder metastasis. He was prescribed combination chemotherapy of cisplatin and tegafur, gimeracil, oteracil potassium, and trastuzumab. After 2 courses of chemotherapy, there was a decrease in the size of both the gastric and bladder lesions. There was also a significant decrease in the tumor marker levels. He is currently alive after 7 courses of chemotherapy.
Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Neoplasias Gástricas/tratamento farmacológico , Neoplasias da Bexiga Urinária/tratamento farmacológico , Humanos , Masculino , Pessoa de Meia-Idade , Neoplasias Gástricas/patologia , Resultado do Tratamento , Neoplasias da Bexiga Urinária/secundárioRESUMO
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 RetrospectivosRESUMO
Peritoneal washing cytology (CY) in patients with pancreatic cancer is mainly used for staging; however, it may also be used to evaluate the intraperitoneal status to predict a more accurate prognosis. Here, we investigated the potential of deep learning of CY specimen images for predicting the 1-year prognosis of pancreatic cancer in CY-positive patients. CY specimens from 88 patients with prognostic information were retrospectively analyzed. CY specimens scanned by the whole slide imaging device were segmented and subjected to deep learning with a Vision Transformer (ViT) and a Convolutional Neural Network (CNN). The results indicated that ViT and CNN predicted the 1-year prognosis from scanned images with accuracies of 0.8056 and 0.8009 in the area under the curve of the receiver operating characteristic curves, respectively. Patients predicted to survive 1 year or more by ViT showed significantly longer survivals by Kaplan-Meier analyses. The cell nuclei found to have a negative prognostic impact by ViT appeared to be neutrophils. Our results indicate that AI-mediated analysis of CY specimens can successfully predict the 1-year prognosis of patients with pancreatic cancer positive for CY. Intraperitoneal neutrophils may be a novel prognostic marker and therapeutic target for CY-positive patients with pancreatic cancer.
Assuntos
Aprendizado Profundo , Neoplasias Pancreáticas , Humanos , Neoplasias Pancreáticas/patologia , Neoplasias Pancreáticas/mortalidade , Neoplasias Pancreáticas/diagnóstico , Feminino , Masculino , Prognóstico , Pessoa de Meia-Idade , Idoso , Estudos Retrospectivos , Redes Neurais de Computação , Curva ROC , Citodiagnóstico/métodos , Estimativa de Kaplan-Meier , Adulto , Lavagem Peritoneal , Idoso de 80 Anos ou mais , Neutrófilos/patologia , CitologiaRESUMO
Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.
Assuntos
Detecção Precoce de Câncer/métodos , Aprendizado de Máquina , Próstata/diagnóstico por imagem , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/patologia , Ultrassonografia/métodos , Idoso , Área Sob a Curva , Diagnóstico Diferencial , Humanos , Masculino , Pessoa de Meia-Idade , Gradação de Tumores , Próstata/patologia , Neoplasias da Próstata/prevenção & controle , Triagem/métodosRESUMO
AIM: We investigated the clinical characteristics and short-term outcomes in acute cholecystitis (AC) patients aged ≥80 years. We therefore sought to determine the ideal treatment for elderly patients with AC. METHODS: We retrospectively evaluated 253 patients with AC. The patients were divided into two groups according to their age: elderly group (n = 77, aged ≥80 years) and non-elderly group (n = 176, aged <80 years). We compared the clinical characteristics, in-hospital mortality and recurrence of cholecystitis within 6 months between the two groups. The predictive factors for in-hospital mortality were also assessed. RESULTS: The elderly group had more severe comorbidities (P = 0.0055), higher severity grade of AC (P = 0.00071) and higher in-hospital mortality (P = 0.029) than the non-elderly group. The multivariate analysis showed that the serum creatinine level (hazard ratio 12.43; P = 0.002) was independently associated with the in-hospital mortality. The elderly group (20.8%) underwent subsequent cholecystectomy less frequently than the non-elderly group (63.2%; P < 0.0001). The recurrence rate of AC was comparable between the two groups (P = 0.89). The proportion of patients in the elderly group who received percutaneous drainage in the latter period (64.3%) was significantly higher than in the former period (33.3%; P = 0.015). CONCLUSIONS: More attention should be paid to AC patients with chronic renal disease after treatment. Percutaneous drainage might serve as a definitive treatment without subsequent cholecystectomy in elderly AC patients with various comorbidities. Geriatr Gerontol Int 2019; 19: 208-212.