CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma

Wakiya, Taiichi; Ishido, Keinosuke; Kimura, Norihisa; Nagase, Hayato; Kanda, Taishu; Ichiyama, Sotaro; Soma, Kenji; Matsuzaka, Masashi; Sasaki, Yoshihiro; Kubota, Shunsuke; Fujita, Hiroaki; Sawano, Takeyuki; Umehara, Yutaka; Wakasa, Yusuke; Toyoki, Yoshikazu; Hakamada, Kenichi

doi:10.1038/s41598-022-12604-8

Cited by 19 publications

(10 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With regard to the ability of radiomics in the prediction of recurrence after treatment, several studies used preoperative or post-operative features [ 144 , 145 , 146 , 147 , 148 , 149 , 150 ]. Jolissant et al predicted ICC recurrence 1 year after surgical treatment by building a model on texture features (TFs) extracted from the liver, from the tumor, and from the future liver remnant (FLR) on preoperative images [ 145 ].…”

Section: Resultsmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

View full text Add to dashboard Cite

Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

show abstract

Section: Resultsmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The DL model allows for complementary feature extraction and cross-scale fusion of multiple MRI sequences, showing better performance than traditional radiomics models for MVI status in ICC patients. In the Wakiya et al ( 66 ). study, early recurrence was defined as recurrence within 1 year after liver surgery.…”

Section: Application Of Deep Learning In Intrahepatic Cholangiocarcinomamentioning

confidence: 99%

Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend

et al. 2023

View full text Add to dashboard Cite

Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.

show abstract

“…This process was performed as described elsewhere. [21] The preoperative axial pancreatic phase of enhanced CT images for each case were obtained from our facility and were used for this study. The spatial resolution of the CT images has been adjusted to 0.0126 mm/pixel.…”

Section: Ct Acquisition and Tumor Segmentationmentioning

confidence: 99%

CT-Based Deep Learning Enables Chemotherapy Response Prediction for Pancreatic Ductal Adenocarcinoma: A Pilot Study

Kanda

Wakiya

Ishido

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Background Predicting response to chemotherapy can lead to its optimization. Furthermore, prediction using a noninvasive approach is clearly preferable to an invasive approach. This study aimed to predict in vitro chemosensitivity assay results by combining computed tomography (CT) images and deep learning (DL) to optimize chemotherapy for pancreatic ductal adenocarcinoma (PDAC) Methods We collected the dataset of preoperative CT images and the histoculture drug response assay (HDRA) of 33 patients undergoing surgery for PDAC at our facility. We trimmed small patches from the entire tumor area, using the data augmentation technique, and obtained 10,730 patches. We established various prediction labels for 5-fluorouracil (FU), gemcitabine (GEM), and paclitaxel (PTX). We built a predictive model using a residual convolutional neural network and used 3-fold cross-validation. Results Of the 33 patients, effective response to FU, GEM, and PTX by HDRA was observed in 19 (57.6%), 11 (33.3%), and 23 (88.5%) patients, respectively. The average accuracy and the area under the receiver operating characteristic curve (AUC) of the model for predicting the effective response to FU were 93.4% and 0.979, respectively. In the prediction of GEM, the models demonstrated high accuracy (92.8%) and AUC (0.969). Likewise, the model for predicting response to PTX had a high performance (accuracy 95.9%, AUC 0.979). Conclusions Our CT-patch-based DL model exhibited high predictive performance in projecting HDRA results. Our study suggests that the DL approach could possibly provide a noninvasive means for the optimization of chemotherapy.

show abstract

CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma

Cited by 19 publications

References 27 publications

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend

CT-Based Deep Learning Enables Chemotherapy Response Prediction for Pancreatic Ductal Adenocarcinoma: A Pilot Study

Contact Info

Product

Resources

About