Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

Yang

Computational and Mathematical Methods in Medicine

et al. 2022

Objective. A case-control study was conducted to explore the efficacy of cohort study and value of CT perfusion imaging in patients with metastatic osteosarcoma after chemotherapy. Methods. Eighty patients with metastatic osteosarcoma treated in our hospital from March 2020 to December 2021 were divided into two groups. According to their different treatment methods, the chemotherapy+antiangiogenesis group had 36 cases and the chemotherapy group had 44 cases. All patients were scanned by 64-slice spiral CT before and after treatment. The differences of tumor volume and perfusion parameters before and after treatment were compared, and the correlation between perfusion parameters and tumor microvessel density (MVD) was analyzed. The receiver working curve (ROC curve) was used to evaluate the efficacy of the two groups after chemotherapy. Results. Blood flow (BF), blood volume (BV), Pallak blood volume (PBV), and time to start (TTS) in the antitumor angiogenesis+chemotherapy group were significantly lower than those before treatment ( P < 0.05 ). Microvessel density was positively correlated with PS, BF, BV, and PBV ( P < 0.05 ). The reduction rate of BV and BF in the remission group after treatment was significantly higher than that in the nonremission group. When the BV and BF decline rates were 47.37% and 21.53% and the areas under the curve were 0.968 and 0.916, respectively, the diagnostic effect was the best. When the decrease rate of BV was 47.48% and the decrease rate of BF was 21.55%, the sensitivity was 94.72% and 89.56% and the specificity was 91.31% and 91.31%. Conclusion. The reduction rate of BV and BF in CT perfusion imaging is of high value in evaluating the efficacy of radiotherapy and chemotherapy in patients with NSCLC and can provide more objective basis for observing the changes and judging the prognosis of osteosarcoma after treatment.

Section: Introductionmentioning

confidence: 99%

A Cohort Study to Evaluate the Efficacy and Value of CT Perfusion Imaging in Patients with Metastatic Osteosarcoma after Chemotherapy

Yang

Computational and Mathematical Methods in Medicine

et al. 2022

“…At least one machine learning validation technique was used in 25 (51%) of the 49 papers. K-fold cross-validation was used in most of the studies [ 13 , 25 , 28 , 31 – 33 , 37 , 38 , 40 , 43 , 44 , 46 – 50 ]. The following machine learning validation techniques were used less commonly: bootstrapping [ 42 , 51 ]; leave-one-out cross-validation [ 34 , 35 , 41 ]; leave-p-out cross-validation [ 52 ]; Monte Carlo cross-validation [ 23 ]; nested cross-validation [ 25 , 27 ]; random-split cross-validation [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

“…GLCM conduces to reflecting the comprehensive information about pixel distribution containing direction, distance, gray value, and the pattern of gray level arrangement (28), and Correlation represents the linear dependency of gray level values to their respective voxels in the GLCM textural features. It has been applied previously in the evaluation of breast cancer, osteosarcoma, lung cancer and gliomas in imaging modalities such as CT, MRI, and PECT (31)(32)(33)(34)(35).…”

Section: Discussionmentioning

confidence: 99%

Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma

et al. 2021

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.MethodWe extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients.ResultsA total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models.ConclusionIn this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.