Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer

Dong, Di; Tang, Lei; Li, Z Y; Fang, Mengjie; Gao, Jianbo; Xu, Shan; Ying, Xiangji; Sun, Ying‐Shi; Fu, Jia; Wang, X X; Li, L M; Li, Z H; Zhang, D F; Zhang, Y.; Li, Z M; Shan, Fei; Bu, Zhaode; Tian, Jie; Ji, Jun

doi:10.1093/annonc/mdz001

Cited by 334 publications

(275 citation statements)

References 19 publications

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“…Radiomics, which involves extracting quantitative features from medical images, is capable of generating imaging biomarkers as decision support tools for clinical practice [18][19][20][21][22][23][24][25][26]. The traditional radiomics method utilizes single-phase medical images for evaluation or prediction, which neglects the tumor change during treatment or following up.…”

Section: Introductionmentioning

confidence: 99%

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

et al. 2020

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Background: The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. Methods: A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). Results:The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. Conclusion:The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.

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Section: Introductionmentioning

confidence: 99%

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

et al. 2020

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“…Using automatic feature extraction algorithms, radiomics is capable of converting embedded information in medical images Abbreviations: CT, computed tomography; PFS, progression-free survival; ALK, anaplastic lymphoma kinase; NSCLC, non-small-cell lung cancer; TKI, tyrosine kinase inhibitor; LASSO, least absolute shrinkage and selection operator; Cindex, concordance index; ASCO, American Society of Clinical Oncology; EGFR, epidermal growth factor receptor; NCCN, National Comprehensive Cancer Network; ICC, interclass correlation coefficient; VOI, volume of interest; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run-length matrix; ROC, receiver operating characteristic; HR, hazard ratio; CI, confidence interval; AUC, area under the curve. into mineable data (16,17), which has been widely applied in the prediction of preoperative distant metastasis, histologic subtype classification, and so on (18)(19)(20). Prognosis based on radiomics is gaining popularity as associations between radiomic features and the underlying genomic patterns emerge in various cancers (21)(22)(23)(24).…”

Section: Introductionmentioning

confidence: 99%

CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study

Zhang

Wang

et al. 2020

Front. Oncol.

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Objectives: To identify a computed tomography (CT)-based radiomic signature for predicting progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC) patients treated with tyrosine kinase inhibitor (TKI) crizotinib. Materials and Methods:This retrospective proof-of-concept study included a cohort of 63 stage IV ALK-positive NSCLC patients who had received TKI crizotinib therapy for model construction and validation. Another independent cohort including 105 stage IV EGFR-positive NSCLC patients was also used for external validation in EGFR-TKI treatment. We initially extracted 481 quantitative three-dimensional features derived from manually segmented tumor volumes of interest. Pearson's correlation analysis along with the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression was successively performed to select critical radiomic features. A CT-based radiomic signature for PFS prediction was obtained using multivariate Cox regression. The performance evaluation of the radiomic signature was conducted using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) analysis, and Kaplan-Meier survival analysis.Results: A radiomic signature containing three features showed significant prognostic performance for ALK-positive NSCLC patients in both the training cohort (C-index, 0.744; time-dependent AUC, 0.895) and the validation cohort (C-index, 0.717; time-dependent AUC, 0.824). The radiomic signature could significantly risk-stratify ALK-positive NSCLC patients (hazard ratio, 2.181; P < 0.001) and outperformed other prognostic factors. However, no significant association with PFS was captured for the radiomic signature in the EGFR-positive NSCLC cohort (log-rank tests, P = 0.41).Li et al. Prognostic Factor for ALK-Positive NSCLC Conclusions:The CT-based radiomic features can capture valuable information regarding the tumor phenotype. The proposed radiomic signature was found to be an effective prognostic factor in stage IV ALK mutated nonsynchronous nodules in NSCLC patients treated with a TKI.

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“…Radiomics and texture analysis may provide promising surrogate parameters for a more objective description. 15 Third, because the scoring system was established using region-to-region comparison, it was theoretically impossible to include cytology status, and because the status of cytology could be obtained using bedside abdominal puncture preoperatively, 16 the status of the cytology was not included in the results.…”

Section: Discussionmentioning

confidence: 99%

Four-Point Computed Tomography Scores for Evaluation of Occult Peritoneal Metastasis in Patients with Gastric Cancer: A Region-to-Region Comparison with Staging Laparoscopy

Tang

et al. 2020

Ann Surg Oncol

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Background. Preoperative diagnosis of peritoneal metastasis with gastric cancer remains challenging. This study explored the abnormal computed tomography (CT) signs of occult peritoneal metastasis (OPM) and evaluated it by region-to-region comparison using staging laparoscopy, from which a 4-point CT score system was developed. Methods. Patients with advanced gastric cancer (stage cT C 2M0) diagnosed by CT were enrolled in the study. Occult peritoneal metastasis detected during staging laparoscopy was compared with preoperative CT to investigate the presence of abnormal signs by a region-toregion comparison. A 4-point CT score system was developed to define the radiologic characteristics. Subsequently, the diagnostic efficacy of the CT score system was prospectively verified. Results. In this study, 57 OPM regions were detected by staging laparoscopy in 33 of the 385 enrolled patients. The greater omentum was the most frequent site of OPM

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Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer

Cited by 334 publications

References 19 publications

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study

Four-Point Computed Tomography Scores for Evaluation of Occult Peritoneal Metastasis in Patients with Gastric Cancer: A Region-to-Region Comparison with Staging Laparoscopy

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