Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients
Abstract Purpose: The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in hepatocellular carcinoma (HCC) patients. Materials and methods: The study population inclu...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-12-01
|
| Series: | Radiation Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13014-022-02138-8 |
| _version_ | 1797863982536589312 |
|---|---|
| author | Anussara Prayongrat Natchalee Srimaneekarn Kanokporn Thonglert Chonlakiet Khorprasert Napapat Amornwichet Petch Alisanant Hiroki Shirato Keiji Kobashi Sira Sriswasdi |
| author_facet | Anussara Prayongrat Natchalee Srimaneekarn Kanokporn Thonglert Chonlakiet Khorprasert Napapat Amornwichet Petch Alisanant Hiroki Shirato Keiji Kobashi Sira Sriswasdi |
| author_sort | Anussara Prayongrat |
| collection | DOAJ |
| description | Abstract Purpose: The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in hepatocellular carcinoma (HCC) patients. Materials and methods: The study population included 201 HCC patients treated with radiotherapy. The patients’ medical records were retrospectively reviewed to obtain the clinical and radiotherapy data. Toxicity was defined by albumin-bilirubin (ALBI) grade increase. The normal liver dose-volume histogram was reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose (2 Gy/fraction and α/β = 2). Three types of ML-based classification models were used, a penalized logistic regression (PLR), random forest (RF), and gradient-boosted tree (GBT) model. Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Internal validation was performed by 5-fold cross validation and external validation was done in 44 new patients. Results: Liver toxicity occurred in 87 patients (43.1%). The best individual model was the GBT model using baseline liver function, liver volume, and MLD as inputs and the best overall model was an ensemble of the PLR and GBT models. An AUROC of 0.82 with a standard deviation of 0.06 was achieved for the internal validation. An AUROC of 0.78 with a standard deviation of 0.03 was achieved for the external validation. The behaviors of the best GBT model were also in good agreement with the domain knowledge on NTCP. Conclusion: We propose the methodology to develop an ML-based NTCP model to estimate the risk of ALBI grade increase. |
| first_indexed | 2024-04-09T22:44:13Z |
| format | Article |
| id | doaj.art-d5e669d05579433eaf670d5a61a1642f |
| institution | Directory Open Access Journal |
| issn | 1748-717X |
| language | English |
| last_indexed | 2024-04-09T22:44:13Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj.art-d5e669d05579433eaf670d5a61a1642f2023-03-22T11:58:24ZengBMCRadiation Oncology1748-717X2022-12-0117111110.1186/s13014-022-02138-8Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patientsAnussara Prayongrat0Natchalee Srimaneekarn1Kanokporn Thonglert2Chonlakiet Khorprasert3Napapat Amornwichet4Petch Alisanant5Hiroki Shirato6Keiji Kobashi7Sira Sriswasdi8Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn UniversityDepartment of Anatomy, Faculty of Dentistry, Mahidol UniversityDivision of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn UniversityDivision of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn UniversityDivision of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn UniversityDivision of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn UniversityGraduate School of Biomedical Science and Engineering, Hokkaido UniversityDepartment of Medical Physics, Hokkaido University HospitalResearch affairs, Faculty of Medicine, Chulalongkorn UniversityAbstract Purpose: The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in hepatocellular carcinoma (HCC) patients. Materials and methods: The study population included 201 HCC patients treated with radiotherapy. The patients’ medical records were retrospectively reviewed to obtain the clinical and radiotherapy data. Toxicity was defined by albumin-bilirubin (ALBI) grade increase. The normal liver dose-volume histogram was reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose (2 Gy/fraction and α/β = 2). Three types of ML-based classification models were used, a penalized logistic regression (PLR), random forest (RF), and gradient-boosted tree (GBT) model. Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Internal validation was performed by 5-fold cross validation and external validation was done in 44 new patients. Results: Liver toxicity occurred in 87 patients (43.1%). The best individual model was the GBT model using baseline liver function, liver volume, and MLD as inputs and the best overall model was an ensemble of the PLR and GBT models. An AUROC of 0.82 with a standard deviation of 0.06 was achieved for the internal validation. An AUROC of 0.78 with a standard deviation of 0.03 was achieved for the external validation. The behaviors of the best GBT model were also in good agreement with the domain knowledge on NTCP. Conclusion: We propose the methodology to develop an ML-based NTCP model to estimate the risk of ALBI grade increase.https://doi.org/10.1186/s13014-022-02138-8Normal tissue complication probability modelMachine learningRadiation-induced liver toxicityAlbumin-bilirubin gradeHepatocellular carcinoma |
| spellingShingle | Anussara Prayongrat Natchalee Srimaneekarn Kanokporn Thonglert Chonlakiet Khorprasert Napapat Amornwichet Petch Alisanant Hiroki Shirato Keiji Kobashi Sira Sriswasdi Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients Radiation Oncology Normal tissue complication probability model Machine learning Radiation-induced liver toxicity Albumin-bilirubin grade Hepatocellular carcinoma |
| title | Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients |
| title_full | Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients |
| title_fullStr | Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients |
| title_full_unstemmed | Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients |
| title_short | Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients |
| title_sort | machine learning based normal tissue complication probability model for predicting albumin bilirubin albi grade increase in hepatocellular carcinoma patients |
| topic | Normal tissue complication probability model Machine learning Radiation-induced liver toxicity Albumin-bilirubin grade Hepatocellular carcinoma |
| url | https://doi.org/10.1186/s13014-022-02138-8 |
| work_keys_str_mv | AT anussaraprayongrat machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT natchaleesrimaneekarn machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT kanokpornthonglert machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT chonlakietkhorprasert machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT napapatamornwichet machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT petchalisanant machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT hirokishirato machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT keijikobashi machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients AT sirasriswasdi machinelearningbasednormaltissuecomplicationprobabilitymodelforpredictingalbuminbilirubinalbigradeincreaseinhepatocellularcarcinomapatients |