Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients

Wang, Qizheng; Zhang, Yang; Zhang, Enlong; Xing, Xiaoying; Chen, Yongye; Su, Min‐Ying; Lang, Ning

doi:10.1016/j.jbo.2021.100354

Cited by 15 publications

(17 citation statements)

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“…Other feature selection methods such as support vector machine or other deep learning-based algorithms, although beyond the scope of this paper, may be explored to further improve model performance (41). Moreover, by constructing multilayer nonlinear complex relationships, deep learning methods are more like a "black-box," making it difficult for physicians to interpret the association of the input images to outcome.…”

Section: Discussionmentioning

confidence: 99%

“…( 40 ) reported that the prediction power of deep learning methods does not necessarily outperform conventional logistic regression in pCR prediction on a data set with 51 locally advanced rectal cancer patients. Other feature selection methods such as support vector machine or other deep learning-based algorithms, although beyond the scope of this paper, may be explored to further improve model performance ( 41 ). Moreover, by constructing multilayer nonlinear complex relationships, deep learning methods are more like a “black-box,” making it difficult for physicians to interpret the association of the input images to outcome.…”

Section: Discussionmentioning

confidence: 99%

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Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II–III Rectal Cancer

Nie

Zheng

et al. 2022

Front. Oncol.

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Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists’ readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II–III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II–III Rectal Cancer

Nie

Zheng

et al. 2022

Front. Oncol.

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“…GLDM as one of the texture features quantifies gray level dependencies in an image, which is defined as the number of connected voxels within a specific distance that is dependent on the center voxel. Texture can reveal tumor heterogeneity, which is relevant to the underlying biology, and radiomics analysis provides a feasible method to unlock the buried information beyond the perception of the human eyes [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

et al. 2022

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Background This retrospective study aimed to develop and validate a combined model based [18F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients. Methods Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [18F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595–0.874) and 0.750 (95% CI, 0.577–0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685–0.916) and 0.869 (95% CI, 0.715–0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794–0.963) and 0.892 (95% CI, 0.758–0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma. Conclusions The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.

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“…In this regard, Wang et al [ 92 ] investigated the role of radiomics analysis on preoperative CT imaging in predicting early postoperative recurrence of 62 patients with spine GCTB. The AUC of the final prediction model using 10 features extracted was 0.78 with an accuracy of 89%, suggesting that the radiomics model has the potential to provide a personalized relapse risk assessment, on whose basis surgery, adjuvant treatments and postoperative follow-up should be assessed.…”

Section: Imaging Contribution In Gctb Managementmentioning

confidence: 99%

State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics

Parmeggiani

Miceli

Errani

et al. 2021

Cancers

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Giant cell tumor of bone (GCTB) is classified as an intermediate malignant tumor due to its locally aggressive behavior, burdened by high local recurrence rate. GCTB accounts for about 4–5% of all primary bone tumors and typically arises in the metaphysis and epiphyses of the long tubular bones. Mutation of gene H3F3A is at the basis of GCTB etiopathogenesis, and its immunohistochemical expression is a valuable method for practical diagnosis, even if new biomarkers have been identified for early diagnosis and for potential tumor recurrence prediction. In the era of computer-aided diagnosis, imaging plays a key role in the assessment of GCTB for surgical planning, patients’ prognosis prediction and post treatment evaluation. Cystic changes, penetrating irregular margins and adjacent soft tissue invasion on preoperative Magnetic Resonance Imaging (MRI) have been associated with a higher rate of local recurrence. Distance from the tumor edge to the articular surface and thickness of unaffected cortical bone around the tumor should be evaluated on Computed Tomography (CT) as related to local recurrence. Main features associated with local recurrence after curettage are bone resorption around the graft or cement, soft tissue mass formation and expansile destruction of bone. A denosumab positive response is represented by a peripherical well-defined osteosclerosis around the lesion and intralesional ossification. Radiomics has proved to offer a valuable contribution in aiding GCTB pre-operative diagnosis through clinical-radiomics models based on CT scans and multiparametric MR imaging, possibly guiding the choice of a patient-tailored treatment. Moreover, radiomics models based on texture analysis demonstrated to be a promising alternative solution for the assessment of GCTB response to denosumab both on conventional radiography and CT since the quantitative variation of some radiomics features after therapy has been correlated with tumor response, suggesting they might facilitate disease monitoring during post-denosumab surveillance.

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Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients

Abstract: Highlights Characteristics of 62 patients with spinal GCTB who underwent surgery. A prognostic classification model was built based on features selected by SVM. The combined histogram and texture features could predict recurrence of GCTB.

Cited by 15 publications

References 43 publications

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II–III Rectal Cancer

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II–III Rectal Cancer

Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics

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