An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics
Insights into Imaging volume 13, Article number: 138 (2022)
To update the systematic review of radiomics in osteosarcoma.
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.
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.
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.
The MRI-radiomics in predicting neoadjuvant chemotherapy response is supported by weak evidence.
The quality of osteosarcoma radiomics studies has been improved recent two years.
CLAIM can adapt the increasing trend of deep learning application in radiomics.
Osteosarcoma is the most common primary high-grade sarcoma of the skeleton, in which the tumor cells produce neoplastic bone . Imaging is the key examination in the work-up of osteosarcoma management, from diagnosis, staging, treatment evaluation, to follow-up [1,2,3]. The diagnosis of osteosarcoma generally starts with X-ray radiography and is followed by CT for further evaluation. A contrast-enhanced MRI scan is useful in diagnosis completion and soft tissue involvement assessment and is usually the last step before biopsy of local disease. A chest CT scan is substantial for lung metastases detection. For patients with pathologically confirmed osteosarcoma, a whole-body PET examination has been recommended for initial staging rather than a bone scintigraphy nowadays. The treatment evaluation and follow-up imaging commonly include local CT and MRI scans and chest CT scan. In most cases, the current imaging approach with physical, laboratory, and histopathological examinations can guide clinicians to an appropriate curation plan, but there remain difficulties in differential diagnosis of osteosarcoma subtypes, prediction of response to treatment, and prognosis concerns including survival, recurrence, and lung metastasis [2, 3]. Radiomics, utilizing a plethora of strategies for extracting underlying information from medical images, has been used to overcome such challenges [4,5,6,7]. Radiomics models have been deemed as a promising approach for addressing clinical problems related to osteosarcoma patients, especially for predicting their response to neoadjuvant chemotherapy (NAC) .
Our preliminary search suggested that radiomics studies in osteosarcoma patients have doubled since the publication of the previous review , indicating necessity for updates on this rapidly developing field. It is unclear whether the radiomics study quality has improved in recent years. Next, the study quality and risk of bias of radiomics research on osteosarcoma have been only assessed by the Radiomics Quality Score (RQS)  and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool . An additional evaluation using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist  has been recommended to identify several significant items for reporting transparency of radiomics studies [11,12,13,14]. Further, RQS and TRIPOD may not be totally suitable for current radiomics studies, since recently developed deep radiomics applies convolutional neural networks to analyze these extracted features [15,16,17,18]. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM)  has been demonstrated as a useful tool to improve design and reporting of deep learning research [20, 21]. It is potentially better for the evaluation of current radiomics studies with increasing application of deep learning. Finally, the level of evidence supporting the radiomics application in osteosarcoma has not been evaluated yet . It is of importance to provide an overall evidence strength rating before translating radiomics into clinical practice [23, 24]. Therefore, we hypothesized that the publication of the previous review could improve the radiomics study quality in osteosarcoma, and that CLAIM is a better tool for current radiomics studies.
The aim of the present study is to provide an updated systematic review of radiomics in osteosarcoma with quality assessment and evidence-level rating and find out whether CLAIM can better identify disadvantages in current radiomics studies.
Protocol and registration
The updating of this systematic review was decided according to a three-step decision framework  and was conducted in the style of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . The review protocol (CRD42020175383) and updating information are present in Additional file 1: Note S1. The PRISMA checklist for current systematic review and meta-analysis is present as Additional file 2.
Literature search and selection
An up-to-date literature search was performed via PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data until May 15, 2022, by two reviewers each with 4 years’ experience in radiology and radiomics research. Disagreements were solved by a review group consisting of radiologists, orthopedists, and pathologists with different levels of experience. All primary research assessing the role of radiomics in osteosarcoma treatment for diagnostic, prognostic, or predictive purposes was considered eligible for the current review. No publication period restrictions were applied, while only articles in English, Japanese, Chinese, German or French were available. The two reviewers screened the titles and abstracts after the removal of duplications and obtained the full-texts and their supplementary materials. The same reviewers determined their eligibility according to the inclusion and exclusion criteria. Other potentially eligible articles were identified from the reference lists of relevant articles and reviews. For uncertainties, the review group was consulted. The search and selection strategy is shown in Additional file 1: Note S2.
Data extraction and quality assessment
We used a data collection sheet for bibliographical information, study characteristics, radiomics considerations, and model metrics (Additional file 1: Table S1) . The eligible studies employed RQS , TRIPOD , CLAIM , and QUADAS-2 tools  (Additional file 1: Tables S2–S5). The RQS is a consensus list composed of sixteen items for methodological issues specific to radiomics studies and is later summarized to six key domains [12,13,14]. The TRIPOD statement provides a checklist consisting of thirty-seven items in twenty-two criteria aiming to promote transparency of prediction model studies and is recommended for identifying room of improvement in radiomics studies [12,13,14]. The CLAIM includes forty-two items in seven topics that should be viewed as a best practice to guide presentation of AI research . The CLAIM has seldomly been employed for quality assessment of radiomics studies [20, 21]. However, we assumed that CLAIM is suitable for radiomics studies evaluation, as radiomics is a subset of AI application in medical imaging [15,16,17,18]. The QUADAS-2 tool was tailored to our review by modifying the signaling questions . Two reviewers independently extracted the data and evaluated the studies. Disagreements were solved by discussion with the review group. Topics discussed are recorded in Additional file 1: Note S3.
Data synthesis and analysis
The statistical analysis was performed with R language version 4.1.3 within RStudio version 1.4.1106 . The RQS rating, the ideal percentage of RQS, and adherence rates of RQS, TRIPOD and CLIAM were calculated. In case a score of at least one point for each item was obtained without minus points, it was considered to have basic adherence, as those have been reported [12,13,14]. For example, if the item of validation in RQS obtained 2–5 points, it was considered as basic adherent, while it was regarded as without basic adherence when it was rated as -5 points. The QUADAS-2 assessment result was summarized. Pearson correlation test was used for the correlation analysis between the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate. Subgroup analysis was performed to compare the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate by journal type, first authorship, imaging modality, and publication period. A two-tailed p value < 0.05 indicated statistical significance, unless otherwise specified. Post hoc multiple comparisons were adjusted using the Bonferroni method. The detailed data analysis method is described in Additional file 1: Note S4.
The meta-analysis was performed using Stata software version 15.1 . In the current systematic review, the role of MRI-driven radiomics in prediction of osteosarcoma patients’ response to NAC was addressed repeatedly. To present the true performance of the radiomics model, corresponding meta-analysis was conducted based on results of testing datasets. The two-by-two tables were directly extracted from the articles or reconstructed based on available data. The diagnostic odds ratio (DOR) with 95% confidence interval (CI) and corresponding p value were calculated using the random-effect model. Sensitivity, specificity, positive and negative likelihood ratio were estimated. A summary receiver operating characteristic (SROC) curve was drawn. The Cochran’s Q test and the Higgins I2 test were used for heterogeneity assessment. The funnel plot was drawn with Egger’s test and Begg’s test, and the Deeks’ funnel plot was constructed with Deeks’ funnel plot asymmetry test for publication bias. A two-tailed p value > 0.10 indicated a low publication bias. The trim and fill method was employed to estimate the number of missing studies. The level of evidence supporting the clinical application of radiomics in osteosarcoma was rated based on results of meta-analysis (Additional file 1: Table S6) .
The search yielded 251 records in total, in which 142 remained after removing duplicates. After screening the titles and abstracts, full-texts of 47 articles were retrieved and reviewed. Ultimately, 29 articles were included in this systematic review [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57] (Fig. 1). No additional eligible study was identified through hand search of relevant reviews and reference lists of eligible articles.
Table 1 and Fig. 2 summarized the characteristics of 33 models described in the 29 included studies. The sample size of studies ranged from 17 to 191 patients with a median of 81 patients. More than a half of studies were published on non-imaging journals (55.2%), while the majority of first authorship belonged to radiologists (65.5%). The most utilized imaging modality was MRI (48.3%). Almost half of the models aimed to predict the response to NAC (48.5%), followed by prognostic models for survival (18.2%) and those for recurrence or metastasis (9.1%). Most models were validated within the same data with or without resampling (48.5%), while a limited number of models were externally validated (12.1%). The detailed characteristics of studies are present in Additional file 1: Tables S7–S10.
Figure 3 summarized the results of study quality evaluation. Table 2 showed that the median (range) of RQS for current osteosarcoma radiomics studies was 10 (3–18), with a percentage of the ideal score of 29.2% (305/1044) and the adherence rate of 44.6% (207/464). Tables 3 and 4 presented that the TRIPOD and CLAIM adherence rates were 59.2% (481/812) and 63.7% (961/1508), respectively. The risk of bias and applicability concerns were mainly related to the index test. The individual assessment for each study is present in Additional file 1: Tables S11–S14.
RQS addressed a radiomics-specific issue of phantom study (0.0%) and the deficiency in cut-off analysis (0.0%) and cost-effectiveness analysis (0.0%). TRIPOD emphasized the shortness in reporting title (6.9%), blindness of assessment for outcomes and predictors (10.3%; 13.7%), and stating study objective in introduction (24.1%). Both RQS and CLAIM indicated a low percentage of comparing the model with the benchmark (27.6%; 27.6%), while both TRIPOD and CLAIM pointed out the disadvantages in sample size or power calculation (10.3%; 13.7%), and missing data handling (20.7%; 20.7%). CLAIM identified extra lacking in reporting in data de-identification (10.3%), stating study hypothesis in introduction (13.8%), and failure analysis (17.2%). All the above three tools emphasized the validation (25/145, 17.2%; 32/64, 50.0%; 16/29, 55.2%) and open science or additional information (10/116, 8.6%; 6/58, 10.3%; 6/87, 6.9%). The correlation between RQS and TRIPOD (r = 0.7498, p < 0.001) was moderate, while that between TRIPOD and CLAIM (r = 0.9004, p < 0.001) and that between RQS and CLAIM (r = 0.8158, p < 0.001) were high (Additional file 1: Fig. S1).
Figure 4 presents results of study quality evaluation with impact factor, sample size, and publication year. We compared the quality of studies published before and after the previous review and found that the ideal percentage of RQS (22.7% vs 33.8%, p = 0.020), the TRIPOD adherence rate (53.6% vs 63.4%, p = 0.026), and the CLAIM adherence rate (56.1% vs 69.1%, p = 0.007) have all been improved (Additional file 1: Table S15 and Additional file 1: Fig. S2). Subgroup analysis also found that imaging modalities utilized in studies have influence on TRIPOD and CLAIM adherence rates (p = 0.002, p = 0.004). The journal type and first authorship did not significantly influence study quality (both p > 0.05).
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). The corresponding metrics indicates a dramatic performance (Additional file 1: Figs. S3–S7). The Cochran’s Q test (Q = 5.18, p = 0.160) and Higgins I-square statistic (I2 = 42.04%) indicated that the heterogeneity was moderate. The funnel plot with Egger’s test (p = 0.035) and Begg’s test (p = 0.089) and the Deeks' funnel plot with Deeks’ asymmetry test (p = 0.069) revealed that the likelihood of publication bias was high (Additional file 1: Figs. S8–S9). The trim and fill analysis estimated that two studies were missing (Additional file 1: Fig. S10). However, the adjusted DOR was 20.53 (95%CI 7.80–54.06; p < 0.001). The level of evidence supporting the application of radiomics in predicting NAC response by MRI is rated as weak (Table 5). All meta-analyzed data are presented in Additional file 1: Table S16.
We provided an updated systematic review on osteosarcoma radiomics. Although the overall methodological and reporting quality of included studies was still suboptimal, it has improved after the publication of the previous review. The evidence supporting MRI-driven radiomics to predict NAC response in osteosarcoma has been rated as weak based on meta-analysis of testing data. CLAIM has shown unique ability in capturing deficiency in radiomics studies with deep learning.
In the previous review, the most frequently investigated question was whether radiomics could predict the NAC response , and it is still the most attractive topic nowadays in osteosarcoma radiomics [29,30,31,32, 35, 38,39,40,41,42,43,44, 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. These achievements cover the routine for osteosarcoma and have potential in aiding clinicians to improve their treatment decision. MRI is currently the most frequently utilized imaging modality, and CT has exceeded PET to become the second. In terms of MRI techniques, T1 mapping and dynamic contrast-enhanced MRI have been introduced into osteosarcoma radiomics studies [31, 55]. 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  for automatic segmentation. These approaches may liberate radiologists from time-consuming segmentation workloads and potentially make osteosarcoma radiomics an automatic pipeline for clinical use. In addition to segmentation, deep learning models have been compared with radiomics models and showed better performance in predicting NAC response and metastasis [41, 42], and the performance of radiomics models improved when incorporating deep learning features . The application of deep learning has not been detected by the previous review, but currently more studies used deep learning to further mine information in images. More studies tested their model using datasets from other institutions [33,34,35, 45] or splitting testing datasets [39, 44, 46,47,48, 50,51,52,53,54,55,56,57] to show the true performance of their models, while none of the studies in the previous review has been externally validated. The improvements in validation settings allow us to meta-analyze the performance of radiomics for prediction of NAC response based on testing datasets. 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 . 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.
Study quality has improved since the publication of the previous review. However, the overall study quality is suboptimal. RQS and TRIPOD have identified disadvantages in phantom study, cut-off analysis, cost-effectiveness analysis, blindness of assessment, sample size calculation, and missing data handling, which have been repeatedly addressed [8, 12,13,14]. The previous review only employed RQS for quality assessment. RQS was a specialized tool proposed to help the radiomics community assess the quality and value of a radiomics study. However, RQS was tailored on hand‐crafted features. As deep learning is gaining momentum, the current version of RQS may not capture the strengths and weaknesses of deep learning radiomics studies correctly . TRIPOD is a similar example that aimed to promote transparency reporting of diagnostic accuracy model studies and has been recommended to identify room for improvements in radiomics studies . Nevertheless, the current version of TRIPOD may not capture some unique challenges with machine learning or AI application . In contrast, CLAIM captured unique shortness in our review, such as data de-identification and failure analysis. CLAIM has been employed as a useful tool for quality evaluation in deep learning studies [20, 21], and our review demonstrated the feasibility of CLAIM in radiomics studies. We further confirmed that CLAIM can serve as a better review and study design guideline in radiomics studies. CLAIM may guide the update of TRIPOD and RQS, because it not only includes general reporting criteria, but also allows extra distinguishment of unique shortness in deep learning. CLAIM may even replace RQS and TRIPOD, considering the overlapping items and high correlation between these tools. The researchers are still reticent in publishing the RQS and TRIPOD for their radiomics studies . Only one study in our review included RQS, TRIPOD and CLAIM as supplementary materials .
Our review has several limitations. First, our review focused on osteosarcoma radiomics studies. The conclusion should be interpreted with caution when expanded to other diseases. However, it provided insights for the design and reporting radiomics studies. Second, our study only included AI research applying the radiomics approach, but overlooked those conducted with only deep learning for segmentation [60,61,62] or modeling . However, the secondary aim of our study is to find out whether CLAIM can better identify disadvantages in radiomics studies than the currently recommended RQS and TRIPOD. As CLAIM is suitable for both radiomics and deep learning radiomics studies, future review is encouraged to carry out without the restriction of radiomics. Third, it has not been investigated in our review how to weigh each item in CLAIM. The previous reviews have created subitems for some evaluations  and weighed them as equal . We treated each subitem as equal, but it is necessary to find out whether it is appropriate. Fourth, we did not employ more specific tools for evaluation, because they are not suitable or are currently under development [59, 64,65,66,67,68]. The review may benefit from the increasing study reporting guidelines for clinical studies using AI in healthcare, because they pay extra attention to additional factors which do not neatly conform to traditional reporting guidelines, especially details relating to technical algorithm development . The Image Biomarkers Standardization Initiative (IBSI) guideline is another potential eligible checklist for quality elevation . However, we did not apply it as previous reviews [14, 24], since this radiomics-specific checklist may not suitable for deep learning studies. Finally, due to the heterogeneity and limited numbers of studies, we only rated the evidence level of radiomics in prediction of NAC response. Further investigation is needed to lay a more robust scientific basis for translating the radiomics approach to a clinical useful tool [23, 24].
In conclusion, the quality of radiomics studies in osteosarcoma has improved in recent years, but is still suboptimal. MRI-driven radiomics for prediction of NAC response in osteosarcoma is rated as weak evidence according to meta-analysis of testing datasets, calling for more high-quality studies to promote radiomics application in osteosarcoma. CLAIM can better identify disadvantages in radiomics studies and therefore is recommended for future evaluation of AI studies including radiomics.
Availability of data and materials
All data generated or analyzed during this study are included in this published article and its supplementary information files.
Checklist for Artificial Intelligence in Medical Imaging
Diagnostic odds ratio
Modified Quality Assessment of Diagnostic Accuracy Studies
Radiomics Quality Score
Summary receiver operating characteristic
Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis
WHO Classification of Tumours Edition Board (2020) World Health organization classification of tumours: WHO classification of tumours of soft tissue and bone, 5th edn. IARC Press, Lyon
Strauss SJ, Frezza AM, Abecassis N et al Guidelines Committee, EURACAN, GENTURIS and ERN PaedCan (2021) Bone sarcomas: ESMO-EURACAN-GENTURIS-ERN PaedCan clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 32(12):1520–1536. https://doi.org/10.1016/j.annonc.2021.08.1995
National Comprehensive Cancer Network (2021) NCCN clinical practice guidelines in oncology: bone cancer, version 2. 2022. https://www.nccn.org/professionals/physician_gls/pdf/bone.pdf. Accessed 8 Oct 2021
Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446. https://doi.org/10.1016/j.ejca.2011.11.036
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169
O’Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186. https://doi.org/10.1038/nrclinonc.2016.162
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762. https://doi.org/10.1038/nrclinonc.2017.141
Zhong J, Hu Y, Si L et al (2021) A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 31(3):1526–1535. https://doi.org/10.1007/s00330-020-07221-w
Whiting PF, Rutjes AW, Westwood ME et al QUADAS-2 Group (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162(1):55–63. https://doi.org/10.7326/M14-0697
Park SH (2022) Guides for the successful conduct and reporting of systematic review and meta-analysis of diagnostic test accuracy studies. Korean J Radiol 23(3):295–297. https://doi.org/10.3348/kjr.2021.0963
Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523–536. https://doi.org/10.1007/s00330-019-06360-z
Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK (2020) Quality reporting of radiomics analysis in mild cognitive impairment and alzheimer’s disease: a roadmap for moving forward. Korean J Radiol 21(12):1345–1354. https://doi.org/10.3348/kjr.2020.0715
Park CJ, Park YW, Ahn SS et al (2022) Quality of radiomics research on brain metastasis: a roadmap to promote clinical translation. Korean J Radiol 23(1):77–88. https://doi.org/10.3348/kjr.2021.0421
Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157. https://doi.org/10.3322/caac.21552
Shur JD, Doran SJ, Kumar S et al (2021) Radiomics in oncology: a practical guide. Radiographics 41(6):1717–1732. https://doi.org/10.1148/rg.2021210037
Cheng PM, Montagnon E, Yamashita R et al (2021) Deep learning: an update for radiologists. Radiographics 41(5):1427–1445. https://doi.org/10.1148/rg.2021200210
Marti-Bonmati L, Koh DM, Riklund K et al (2022) Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper. Insights Imaging 13:89. https://doi.org/10.1186/s13244-022-01220-9
Mongan J, Moy L, Kahn CE Jr (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2(2):e200029. https://doi.org/10.1148/ryai.2020200029
O’Shea RJ, Sharkey AR, Cook GJR, Goh V (2021) Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol 31(10):7969–7983. https://doi.org/10.1007/s00330-021-07881-2
Si L, Zhong J, Huo J, et al. (2022) Deep learning in knee imaging: a systematic review utilizing a checklist for artificial intelligence in medical imaging (CLAIM). Eur Radiol 32(2):1353–1361. https://doi.org/10.1007/s00330-021-08190-4
Dang Y, Hou Y (2021) The prognostic value of late gadolinium enhancement in heart diseases: an umbrella review of meta-analyses of observational studies. Eur Radiol 31(7):4528–4537. https://doi.org/10.1007/s00330-020-07437-w
Gitto S, Cuocolo R, Albano D et al (2021) CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 12(1):68. https://doi.org/10.1186/s13244-021-01008-3
Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M (2020) Systematic review of sarcomas radiomics studies: bridging the gap between concepts and clinical applications? Eur J Radiol 132:109283. https://doi.org/10.1016/j.ejrad.2020.109283
Garner P, Hopewell S, Chandler J et al Panel for updating guidance for systematic reviews (PUGs) (2016) When and how to update systematic reviews: consensus and checklist. BMJ 354:i3507. https://doi.org/10.1136/bmj.i3507
Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71
Mangiafico SS (2016) Summary and analysis of extension program evaluation in R, version 1.19.10. http://rcompanion.org/handbook/. Accessed May 2022
Cochrane screening and diagnostic test methods group (2022) Cochrane handbook for systematic reviews of diagnostic test accuracy, version 2. https://training.cochrane.org/handbook-diagnostic-test-accuracy. Accessed May 2022
Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2019) Intravoxel incoherent motion (IVIM) for response assessment in patients with osteosarcoma undergoing neoadjuvant chemotherapy. Eur J Radiol 119:108635. https://doi.org/10.1016/j.ejrad.2019.08.004
Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A (2021) Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging. NMR Biomed 34(2):e4426. https://doi.org/10.1002/nbm.4426
Baidya Kayal E, Sharma N, Sharma R, Bakhshi S, Kandasamy D, Mehndiratta A (2022) T1 mapping as a surrogate marker of chemotherapy response evaluation in patients with osteosarcoma. Eur J Radiol 148:110170. https://doi.org/10.1016/j.ejrad.2022.110170
Bailly C, Leforestier R, Campion L et al (2017) Prognostic value of FDG-PET indices for the assessment of histological response to neoadjuvant chemotherapy and outcome in pediatric patients with Ewing sarcoma and osteosarcoma. PLoS One 12(8):e0183841. https://doi.org/10.1371/journal.pone.0183841
Chen H, Liu J, Cheng Z et al (2020) Value of radiomics nomogram based on T1WI for pretreatment prediction of relapse within 1 year in osteosarcoma: a multicenter study. Chin J Radiol 54(9):874–881. https://doi.org/10.3760/cma.j.cn112149-20200512-00675 (in Chinese)
Chen H, Liu J, Cheng Z et al (2020) Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: a retrospective multicenter study. Eur J Radiol 129:109066. https://doi.org/10.1016/j.ejrad.2020.109066
Chen H, Zhang X, Wang X et al (2021) MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol 31(10):7913–7924. https://doi.org/10.1007/s00330-021-07748-6
Cho YJ, Kim WS, Choi YH et al (2019) Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: differentiation of pulmonary metastases from non-metastatic nodules. PLoS One 14(2):e0211969. https://doi.org/10.1371/journal.pone.0211969
Dai Y, Yin P, Mao N et al (2020) Differentiation of pelvic osteosarcoma and ewing sarcoma using radiomic analysis based on T2-weighted images and contrast-enhanced T1-weighted images. Biomed Res Int 2020:9078603. https://doi.org/10.1155/2020/9078603
Djuričić GJ, Ahammer H, Rajković S (2022) Directionally sensitive fractal radiomics compatible with irregularly shaped magnetic resonance tumor regions of interest: association with osteosarcoma chemoresistance. J Magn Reson Imaging. https://doi.org/10.1002/jmri.28232
Dufau J, Bouhamama A, Leporq B et al (2019) Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data. Bull Cancer 106(11):983–999. https://doi.org/10.1016/j.bulcan.2019.07.005 (in French)
Jeong SY, Kim W, Byun BH et al (2019) prediction of chemotherapy response of osteosarcoma using baseline 18F-FDG textural features machine learning approaches with PCA. Contrast Media Mol Imaging 2019:3515080. https://doi.org/10.1155/2019/3515080
Kim BC, Kim J, Kim K et al (2021) Preliminary radiogenomic evidence for the prediction of metastasis and chemotherapy response in pediatric patients with osteosarcoma using 18F-FDF PET/CT, EZRIN and KI67. Cancers (Basel) 13(11):2671. https://doi.org/10.3390/cancers13112671
Kim J, Jeong SY, Kim BC et al (2021) Prediction of neoadjuvant chemotherapy response in osteosarcoma using convolutional neural network of tumor center 18F-FDG PET images. Diagnostics (Basel) 11(11):1976. https://doi.org/10.3390/diagnostics11111976
Lee SK, Jee WH, Jung CK et al (2020) Prediction of poor responders to neoadjuvant chemotherapy in patients with osteosarcoma: additive value of diffusion-weighted MRI including volumetric analysis to standard MRI at 3T. PLoS One 15(3):e0229983. https://doi.org/10.1371/journal.pone.0229983
Lin P, Yang PF, Chen S et al (2020) A delta-radiomics model for preoperative evaluation of neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 20(1):7. https://doi.org/10.1186/s40644-019-0283-8
Liu J, Lian T, Chen H et al (2021) Pretreatment prediction of relapse risk in patients with osteosarcoma using radiomics nomogram based on CT: a retrospective multicenter study. Biomed Res Int 2021:6674471. https://doi.org/10.1155/2021/6674471
Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W (2022) Radiomics analysis of multiparametric MRI for prediction of synchronous lung metastases in osteosarcoma. Front Oncol 12:802234. https://doi.org/10.3389/fonc.2022.802234
Pereira HM, Leite Duarte ME, Ribeiro Damasceno I, de Oliveira Moura Santos LA, Nogueira-Barbosa MH (2021) Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma. Br J Radiol 94(1124):20201391. https://doi.org/10.1259/bjr.20201391
Sheen H, Kim W, Byun BH et al (2019) Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: a multivariable radiomics model. PLoS One 14(11):e0225242. https://doi.org/10.1371/journal.pone.0225242
Song H, Jiao Y, Wei W et al (2019) Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy? Eur Radiol 29(7):3945–3954. https://doi.org/10.1007/s00330-019-06074-2
Wan Y, Yang P, Xu L et al (2021) Radiomics analysis combining unsupervised learning and handcrafted features: a multiple-disease study. Med Phys 48(11):7003–7015. https://doi.org/10.1002/mp.15199
Wu Y, Xu L, Yang P et al (2018) Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine 34:27–34. https://doi.org/10.1016/j.ebiom.2018.07.006
Xu L, Yang P, Yen EA et al (2019) A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol 64(21):215009. https://doi.org/10.1088/1361-6560/ab489f
Xu L, Yang P, Hu K et al (2021) Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images. Quant Imaging Med Surg 11(4):1184–1195. https://doi.org/10.21037/qims-20-681
Yin P, Zhi X, Sun C et al (2021) Radiomics models for the preoperative prediction of pelvic and sacral tumor types: a single-center retrospective study of 795 cases. Front Oncol 11:709659. https://doi.org/10.3389/fonc.2021.709659
Zhang L, Ge Y, Gao Q et al (2021) Machine learning-based radiomics nomogram with dynamic contrast-enhanced MRI of the osteosarcoma for evaluation of efficacy of neoadjuvant chemotherapy. Front Oncol 11:758921. https://doi.org/10.3389/fonc.2021.758921
Zhao S, Su Y, Duan J et al (2019) Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma. J Bone Oncol 19:100263. https://doi.org/10.1016/j.jbo.2019.100263
Zhong J, Zhang C, Hu Y et al (2022) Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram. Eur Radiol. https://doi.org/10.1007/s00330-022-08735-1
Guiot J, Vaidyanathan A, Deprez L et al (2022) A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev 42(1):426–440. https://doi.org/10.1002/med.21846
Collins GS, Dhiman P, Andaur Navarro CL et al (2021) Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11(7):e048008. https://doi.org/10.1136/bmjopen-2020-048008
Huang L, Xia W, Zhang B, Qiu B, Gao X (2017) MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images. Comput Methods Progr Biomed 143:67–74. https://doi.org/10.1016/j.cmpb.2017.02.013
Zhang R, Huang L, Xia W, Zhang B, Qiu B, Gao X (2018) Multiple supervised residual network for osteosarcoma segmentation in CT images. Comput Med Imaging Graph 63:1–8. https://doi.org/10.1016/j.compmedimag.2018.01.006
Wu J, Yang S, Gou F et al (2022) Intelligent segmentation medical assistance system for MRI images of osteosarcoma in developing countries. Comput Math Methods Med 2022:7703583. https://doi.org/10.1155/2022/7703583
Huang B, Wang J, Sun M et al (2020) 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 20(1):322. https://doi.org/10.1186/s12885-020-06825-1
Sounderajah V, Ashrafian H, Rose S et al (2021) A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 27(10):1663–1665. https://doi.org/10.1038/s41591-021-01517-0
Vasey B, Nagendran M, Campbell B et al DECIDE-AI expert group (2022) Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 28(5):924–933. https://doi.org/10.1038/s41591-022-01772-9
Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ, SPIRIT-AI and CONSORT-AI Working Group; SPIRIT-AI and CONSORT-AI Steering Group; SPIRIT-AI and CONSORT-AI Consensus Group (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26(9):1351–1363. https://doi.org/10.1038/s41591-020-1037-7
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Working Group (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26(9):1364–1374. https://doi.org/10.1038/s41591-020-1034-x
Sounderajah V, Ashrafian H, Aggarwal R et al (2020) Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI steering group. Nat Med 26(6):807–808. https://doi.org/10.1038/s41591-020-0941-1
Shelmerdine SC, Arthurs OJ, Denniston A, Sebire NJ (2021) Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health Care Inform 28(1):e100385. https://doi.org/10.1136/bmjhci-2021-100385
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145
The authors would like to express their gratitude to Dr. Jia Geng and Dr. Liping Si for their contribution on the previous version of this systematic review and meta-analysis; and Dr. Shiqi Mao for his suggestions on data visualization.
This study has received funding by Yangfan Project of Science and Technology Commission of Shanghai Municipality (22YF1442400); Shanghai Science and Technology Commission Science and Technology Innovation Action Clinical Innovation Field (18411953000); Medicine and Engineering Combination Project of Shanghai Jiao Tong University (YG2019ZDB09); and Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine (TRKYRC-XX202204, 2020TRYJ(LB)06, 2020TRYJ(JC)07, TRGG202101, TRYJ2021JC06). They played no role in the study design, data collection or analysis, decision to publish, or manuscript preparation.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
. Note S1: Review protocol. Note S2: Search strategy and study selection. Note S3: Consensus reached during data extraction and quality assessment. Note S4: Data synthesis and analysis methods. Table S1: Data extraction sheet. Table S2: RQS elements according to six key domains. Table S3: TRIPOD reporting completeness checklist. Table S4: CLAIM for authors and reviewers. Table S5: QUADAS-2 tool for risk of bias and concern on application. Table S6: Category of five levels of supporting evidence of meta-analyzes. Table S7: Study characteristics of included studies. Table S8: PICOT of included studies. Table S9: Radiomics methodological issue of included studies. Table S10: Model presentation and performance metrics of included studies. Table S11: RQS rating per study. Table S12: TRIPOD adherence per study. Table S13: CLAIM adherence per study. Table S14: QUADAS-2 assessment per study. Table S15: Subgroup analysis of study quality according to study characteristics. Table S16: Model metrics of studies included in meta-analysis. Figure S1: Correlation between quality evaluation tools. Figure S2: Subgroup analysis of quality evaluation results. Figure S3: Forrest plot of pooled sensitivity. Figure S4: Forrest plot of pooled specificity. Figure S5: Forrest plot of pooled positive likelihood ratio. Figure S6: Forrest plot of pooled negative likelihood ratio. Figure S7: SROC curve of the model performance. Figure S8: Funnel plot of studies included in meta-analysis. Figure S9: Deeks' funnel plot of studies included in meta-analysis. Figure S10: Trim and fill analysis of studies included in meta-analysis.
. PRISMA 2020 checklist.
About this article
Cite this article
Zhong, J., Hu, Y., Zhang, G. et al. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging 13, 138 (2022). https://doi.org/10.1186/s13244-022-01277-6