Development and validation of a MRI-based combined radiomics nomogram for differentiation in chondrosarcoma

Li, Xiaofen; Lan, Min; Wang, Xiaolian; Zhang, Jingkun; Gong, Lirong; Li, Fengxiang; Lin, Huashan; Dai, Shun; Fan, Bing; Dong, Wentao

doi:10.3389/fonc.2023.1090229

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A clinical validation of the radiomics-based prediction model was reported in 38 (69%) of the 55 studies. In 22 (40%) studies, it was performed on a separate set of data from the primary institution, namely the internal test dataset, which was chosen randomly [ 19 , 20 , 24 , 28 , 29 , 31 , 32 , 37 , 41 , 42 , 45 , 47 , 52 , 53 , 59 , 65 , 66 , 68 ], based on temporal criteria [ 61 , 69 , 70 ] or different acquisition scanners [ 62 ]. Of note, in a multi-center study, patients were split into training and test cohorts randomly rather than following geographical criteria [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

Gitto,

Cuocolo,

Huisman

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objective To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. Methods A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. Results Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. Conclusions Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. Critical relevance statement An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. Key points • 2021–2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation. Graphical Abstract

show abstract

Section: Resultsmentioning

confidence: 99%

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

Gitto,

Cuocolo,

Huisman

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

show abstract

“…Неоднорідне або однорідне посилення високодиференційованих форм ХС корелює з клітинними ділянками за мікроскопічного дослідження [67]. Крім того, МРТвізуалізація з контрастним посиленням може допомогти розрізнити енхондроми та ХС [68].…”

unclassified

Chondrosarcoma in the Xxi Century

Vyrva¹

2023

OTP

View full text Add to dashboard Cite

Chondrosarcoma (CHS) is a rare oncopathology, is the third most common primary bone tumor after multiple myeloma and osteosarcoma. It accounts for about 25 % of the total number of bone sarcomas. CHS mainly affects adults and occurs more often in people older than 40 years, in children and adolescents it is less than 5 % of all cases of primary CHS. The most common CHS sites can be any bone containing cartilage, but most often this tumor is found in pelvis, femur and shoulder bones, and ribs. CHS are divided: by origin (primary and secondary), anatomical site (central — inside the bone marrow canal, peripheral — inside the existing osteochondroma, periosteal — on the bone surface), histological degrees GI-GII-GIII. The WHO classification (2020) includes central normal, secondary peripheral, periosteal, dedifferentiated, mesenchymal and clear-cell CHS. More than 90 % of conventional CHS are tumors of low and medium malignancy degree, with a low potential for metastasis. CHS is caused by mutations in genes that control bone growth and development. The main risk factors are the patientʼs age, previous radiation, genetic factors and predisposition to oncological diseases. Diagnosis of CHS is based on a complex algorithm, which involves collecting the patient's complaints, anamnesis, clarifying clinical symptoms, imaging (X-ray, CT, MRI), histopathological picture. The biopsy conclusion is the most important in establishing the final diagnosis. However, there are several tumors whose histological picture is similar to CHS: enchondroma, chondroblastoma, osteosarcoma, giant cell tumor of bone, dedifferentiated liposarcoma, synovial sarcoma. The CHS treatment protocol is determined based on the results of anamnestic data, imaging, histopathological results, CHS classification, and its final tumor subtype. The «gold standard» remains surgical removal of the tumor. Radiation and chemotherapy don’t play a significant role in the treatment of CHS, but require further study. Targeted and immunotherapy have a certain potential, even with a high degree of CHS resistance to traditional chemotherapy.

show abstract

Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

Yang,

Bao

et al. 2024

Cancer Medicine

View full text Add to dashboard Cite

BackgroundChondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).ResultsThe study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.ConclusionsThis study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

show abstract

Development and validation of a MRI-based combined radiomics nomogram for differentiation in chondrosarcoma

Cited by 5 publications

References 24 publications

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

Chondrosarcoma in the Xxi Century

Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

Contact Info

Product

Resources

About