Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram

Zhong, Jingyu; Zhang, Chengxiu; Hu, Yangfan; Zhang, Jing; Liu, Yun; Si, Lin; Xing, Yue; Ding, Defang; Geng, Jia; Jiao, Qiong; Zhang, Huizhen; Yang, Guang; Yao, Weiwu

doi:10.1007/s00330-022-08735-1

Cited by 24 publications

(25 citation statements)

References 37 publications

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“…The meta-analysis of radiomics predicting NAC response by MRI presented a DOR of 28.83 (95%CI 10.27–80.95) on testing datasets of 115 osteosarcoma patients in total [ 35 , 39 , 55 , 57 ] (Fig. 5 ).…”

Section: Resultsmentioning

confidence: 99%

“…After screening the titles and abstracts, full-texts of 47 articles were retrieved and reviewed. Ultimately, 29 articles were included in this systematic review [ 29 – 57 ] (Fig. 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…In the previous review, the most frequently investigated question was whether radiomics could predict the NAC response [ 8 ], and it is still the most attractive topic nowadays in osteosarcoma radiomics [ 29 – 32 , 35 , 38 – 44 , 49 , 53 , 55 , 57 ]. The current review identified two studies each for differential diagnosis [ 37 , 54 ], for metastasis at diagnosis [ 46 , 47 ], and for early recurrence [ 33 , 34 ] of osteosarcoma, while none of the previous twelve studies touched upon these topics.…”

Section: Discussionmentioning

confidence: 99%

“…The pooled DOR is lower than that in the previous review (28.83 vs 43.68), but result of the present review is more robust and interpretable [ 23 ]. We only included MRI-driven radiomics models which have been evaluated on testing datasets [ 35 , 39 , 55 , 57 ], while the previous meta-analysis was carried out based on any imaging modality or dataset.…”

Section: Discussionmentioning

confidence: 99%

“…However, whether these advanced techniques allow radiomics to better answer the clinical questions has not been fully investigated. Although most of studies segmented ROIs manually, two studies and one study, respectively, employed the region growing method based on the threshold of SUV [ 40 , 42 ] and a deep learning nnU-Net [ 57 ] for automatic segmentation. These approaches may liberate radiologists from time-consuming segmentation workloads and potentially make osteosarcoma radiomics an automatic pipeline for clinical use.…”

Section: Discussionmentioning

confidence: 99%

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An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

Zhong

Zhang

et al. 2022

Insights Imaging

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Objective To update the systematic review of radiomics in osteosarcoma. Methods PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 2022. The studies were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in Medical Imaging (CLAIM), and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results. Results Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence. Conclusions The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research.

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Section: Resultsmentioning

confidence: 99%

“…After screening the titles and abstracts, full-texts of 47 articles were retrieved and reviewed. Ultimately, 29 articles were included in this systematic review [ 29 – 57 ] (Fig. 1 ).…”

Section: Resultsmentioning

confidence: 99%