Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

Choi, Sehoon; Park, K.J.; Heo, Changhoe; Park, B.W.; Kim, M.; Kim, J.K.

doi:10.1016/j.crad.2021.03.001

Cited by 15 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, because micro-metastases are not identified by the current radiological techniques, improving the accuracy in detecting pulmonary metastases at the time of diagnosis is necessary [ 4 ]. Radiomics analysis, which refers to extracting radiomics features from medical images and then transferring them into high-dimensional data, has been applied in various types of tumors for diagnosis and prognosis and as a treatment response imaging biomarker [ 5 – 7 ]. Few studies have employed radiomics to identify patients at risk for developing pulmonary metastases [ 8 , 9 ], and Dai et al used multiparametric magnetic resonance imaging (MRI)-based radiomic analysis for the distinction of ewing sarcoma and osteosarcoma [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma

Liu,

Yin,

Cui

et al. 2023

BMC Med Imaging

View full text Add to dashboard Cite

Objectives This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis. Materials and methods A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models. Results Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models. Conclusions In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.

show abstract

Section: Introductionmentioning

confidence: 99%

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma

Liu,

Yin,

Cui

et al. 2023

BMC Med Imaging

View full text Add to dashboard Cite

show abstract

“…CT imaging has a fundamental role in the diagnosis, staging, treatment guidance, and response monitoring in BCa [ 3 ]. As imaging-based features can be easily obtained non-invasively at low costs, radiomics has been increasingly evaluated for initial detection, grading, and local as well as nodal staging of BCa patients [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], for recurrence assessment [ 31 , 32 , 33 ] and for the response evaluation to neoadjuvant preoperative as well as adjuvant treatments in recent years [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. However, to our knowledge, no imaging-based machine-learning models have been investigated for outcome prediction of BCa patients before RC.…”

Section: Discussionmentioning

confidence: 99%

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy

Woźnicki

Laqua

Messmer

et al. 2022

Cancers

View full text Add to dashboard Cite

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48–74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657–0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617–0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

show abstract

“…C. Shen et al constructed an immune-associated two-gene signature to predict MIBC patients’ response to immunotherapy, succeeding with an AUC value of 0.695 in terms of its predictive ability [ 146 ]. S. J. Choi et al developed a radiomic-based model for predicting the response of MIBC patients to neoadjuvant chemotherapy (NAC), achieving an AUC value of 0.75 for the validation set [ 147 ]. In a similar line, A. Parmar et al used a predictive radiomic signature for MIBC patients’ response to NAC, reaching an AUC value of 0.63 in terms of discriminating the patients into responders and non-responders [ 148 ].…”

Section: Discussionmentioning

confidence: 99%

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Sarafidis

Lambrou

Zoumpourlis

et al. 2022

Cancers

View full text Add to dashboard Cite

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

show abstract

Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

Cited by 15 publications

References 46 publications

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Contact Info

Product

Resources

About