지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 27 - 35 (9page)
이용수
초록· 키워드
오류제보하기
Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy.
Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses.
Results: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605–0.976; P<0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8–22.2), and 14.8 months (95% CI, 14.2–15.4), respectively (P=0.431, log-rank test).
Conclusion: Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.
Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses.
Results: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605–0.976; P<0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8–22.2), and 14.8 months (95% CI, 14.2–15.4), respectively (P=0.431, log-rank test).
Conclusion: Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications
Investigative Magnetic Resonance Imaging
2021 .12
-
CT Radiomics in Thoracic Oncology: Technique and Clinical Applications
Nuclear medicine and molecular imaging : NMMI
2018 .01
-
Application of radiomics for GI cancer imaging
대한임상종양학회 학술대회지
2022 .02
-
Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies
Nuclear medicine and molecular imaging : NMMI
2018 .01
-
Overall Survival Depending on Primary Tumor Location in Colorectal Cancer Liver Metastases
대한외과학회 학술대회 초록집
2022 .11
-
EDITORIAL : Radiomics and Deep Learning in Clinical Imaging: What Should We Do?
Nuclear medicine and molecular imaging : NMMI
2018 .01
-
Living donor liver transplantation for unresectable colorectal liver metastasis: Report of a case with 13-year follow-up without recurrence
Annals of Hepato-Biliary-Pancreatic Surgery
2020 .05
-
Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics
Yonsei Medical Journal
2023 .05
-
The impact of systematic multidisciplinary tumor board in the management of patients with colorectal liver metastases: A ten-year analysis
Annals of Hepato-Biliary-Pancreatic Surgery
2022 .12
-
How successful is liver resection for colorectal cancer liver metastases in patients over 75 years old?
Annals of Hepato-Biliary-Pancreatic Surgery
2021 .02
-
Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach
Yonsei Medical Journal
2020 .01
-
Outcomes of simultaneous major liver resection and colorectal surgery for colorectal liver metastases
한국간담췌외과학회 학술대회지
2015 .09
-
Meta-analysis of oncologic effect of primary tumor resection in patients with unresectable stage IV colorectal cancer in the era of modern systemic chemotherapy
Annals of Surgical Treatment and Research
2018 .08
-
Radiofrequency Ablation in Treating Colorectal Cancer Patients with Liver Metastases
Asian Pacific journal of cancer prevention : APJCP
2015 .01
-
Comparison of Tumor Burden Score and Survival of Colorectal Cancer Liver Metastasis Patients according to Treatment Modality of Liver Metastasis
대한외과학회 학술대회 초록집
2022 .11
-
컴퓨터 단층 촬영 영상에서의 전이성 척추 종양의 정량적 분류를 위한 라디오믹스 기반의 머신러닝 기법
의공학회지
2021 .01
-
Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer
ULTRASONOGRAPHY
2021 .01
-
Preliminary data on computed tomography-based radiomics for predicting programmed death ligand 1 expression in urothelial carcinoma
고신대학교 의과대학 학술지
2024 .09
-
Neoadjuvant chemotherapy might be better treatment strategy for patients with initially resectable colorectal liver metastases
대한임상종양학회 학술대회지
2021 .06
-
Streamline flow of the portal vein affects the lobar distribution of colorectal liver metastases and has a clinical impact on survival
Annals of Surgical Treatment and Research
2017 .05
최근 본 자료
댓글(0)
0