Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study
Abstract Background Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour nec...
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| Format: | Article |
| Language: | English |
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BMC
2020-04-01
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| Series: | BMC Cancer |
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| Online Access: | http://link.springer.com/article/10.1186/s12885-020-06825-1 |
| _version_ | 1828453489997512704 |
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| author | Bingsheng Huang Jifei Wang Meili Sun Xin Chen Danyang Xu Zi-Ping Li Jinting Ma Shi-Ting Feng Zhenhua Gao |
| author_facet | Bingsheng Huang Jifei Wang Meili Sun Xin Chen Danyang Xu Zi-Ping Li Jinting Ma Shi-Ting Feng Zhenhua Gao |
| author_sort | Bingsheng Huang |
| collection | DOAJ |
| description | Abstract Background Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated. Methods Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas (non-cartilaginous tumour necrosis areas, post-necrotic tumour collagen areas, and tumour necrotic cystic/haemorrhagic and secondary aneurismal bone cyst areas). The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation. Results For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (P = 0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P < 0.05). For distinguishing cartilaginous tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.61, while the classifier with multi-parametric MRI parameters improved to 0.81(P < 0.05). Conclusions The combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma. |
| first_indexed | 2024-12-11T00:08:08Z |
| format | Article |
| id | doaj.art-cc0bf7b9bb5a42fa843d00fb572d2e63 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-11T00:08:08Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
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| spelling | doaj.art-cc0bf7b9bb5a42fa843d00fb572d2e632022-12-22T01:28:16ZengBMCBMC Cancer1471-24072020-04-012011910.1186/s12885-020-06825-1Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary studyBingsheng Huang0Jifei Wang1Meili Sun2Xin Chen3Danyang Xu4Zi-Ping Li5Jinting Ma6Shi-Ting Feng7Zhenhua Gao8Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen UniversityDepartment of Medical Imaging and Interventional Radiology, Sun Yat-Sen University Cancer Centre, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer MedicineNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Medicine, Shenzhen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen UniversityMedical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen UniversityDepartment of Radiology, the First Affiliated Hospital, Sun Yat-Sen UniversityAbstract Background Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated. Methods Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas (non-cartilaginous tumour necrosis areas, post-necrotic tumour collagen areas, and tumour necrotic cystic/haemorrhagic and secondary aneurismal bone cyst areas). The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation. Results For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (P = 0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P < 0.05). For distinguishing cartilaginous tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.61, while the classifier with multi-parametric MRI parameters improved to 0.81(P < 0.05). Conclusions The combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma.http://link.springer.com/article/10.1186/s12885-020-06825-1OsteosarcomaRandom forestMRINeoadjuvant chemotherapy |
| spellingShingle | Bingsheng Huang Jifei Wang Meili Sun Xin Chen Danyang Xu Zi-Ping Li Jinting Ma Shi-Ting Feng Zhenhua Gao Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study BMC Cancer Osteosarcoma Random forest MRI Neoadjuvant chemotherapy |
| title | Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study |
| title_full | Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study |
| title_fullStr | Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study |
| title_full_unstemmed | Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study |
| title_short | Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study |
| title_sort | feasibility of multi parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy a preliminary study |
| topic | Osteosarcoma Random forest MRI Neoadjuvant chemotherapy |
| url | http://link.springer.com/article/10.1186/s12885-020-06825-1 |
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