Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

Abstract

Objective. To explore the CT radiomic features and clinical imaging features of the primary tumor in patients with nonsmall cell lung cancer (NSCLC) before treatment and their predictive value for the occurrence of bone metastases. Methods. From June 2017 to June 2021, 195 patients with NSCLC who were pathologically diagnosed without any treatment in the Cancer Hospital Affiliated to Hainan Medical College were retrospectively analyzed, and they were divided into a bone metastasis group and a nonbone metastasis group. The relationship between clinical imaging features and bone metastasis in patients was analyzed by the t-test, rank sum test, and χ2 test. At the same time, ITK software was used to extract the radiomic characteristics of the primary tumor of the patients, and the patients were randomly divided into a training group and a validation group in a ratio of 7 : 3. The training model was validated in the validation group, and the performance of the model for predicting bone metastases in NSCLC patients was verified by the ROC curve, and a multivariate logistic regression prediction model was established based on the omics parameters extracted from the best prediction model combined with clinical image features. Results. Seven features were screened from the primary tumor by LASSO to establish a model for predicting metastasis. The area under the curve was 0.82 and 0.73 in the training and validation sets. The best omics signature and univariate analysis suggested clinical imaging factors P < 0.05 associated with bone metastases were included in multivariate binary logistic analysis to obtain clinical characteristics of the primary tumor such as gender (OR = 0.141, 95% CI: 0.022–0.919, P = 0.04), increased Cyfra21-1 (OR = 0.12, 95% CI: 0.018–0.782, P = 0.027), Fe content in blood (OR = 0.774, 95% CI: 0.626–0.958, P = 0.018), CT signs such as lesion homogeneity (OR = 0.052, 95% CI: 0.006–0.419, P = 0.006), pleural indentation sign (OR = 0.007, 95% CI: 0.001–0.696, P = 0.034), and omics characteristics glszm_Small Area High Gray Level Emphasis (OR = 0.016, 95% CI: 0.001–0.286, P = 0.005) were independent risk factors for bone metastasis in patients. Conclusion. The prediction model established based on radiomics and clinical imaging features has high predictive performance for the occurrence of bone metastasis in NSCLC patients.

Research status and progress of radiomics in bone and soft tissue tumors: A review

“…Chen et al [68] 2021 Retrospective STS MR 62 AUC = 0.791 C-index = 0.781 Spraker et al [69] 2019 Retrospective STS MR 226 C-index = 0.78 Giraudo et al [70] 2022 Retrospective STS MR 36 Accuracy = 76.7% Tagliafico et al [71] 2019 Prospective STS MR 11 AUC = 0.96 Wei et al [78] 2019 Retrospective Chordoma MR 148 Accuracy = 82.4% Zhai et al [79] 2022 Retrospective Chordoma MR 174 AUC = 0.747, 0.807, 0.904 AUC = 0.582, 0.852, 0.914 Wang et al [80] 2021 Retrospective Giant cell tumor of bone CT 62 AUC = 0.78 Accuracy = 89% Yin et al [81] 2019 Retrospective Chondrosarcoma MR 103 AUC = 0.891 ACC = 0.857 Hinzpeter et al [82] 2022 Retrospective Prostate cancer bone metastases PET/CT 67 Accuracy = 90% Sensitivity = 91% Specificity = 88% Wang et al [83] 2019 Retrospective Prostate cancer bone metastases MR 176 AUC = 0.898 Zhang et al [84] 2020 Retrospective Prostate cancer bone metastases MR 116 AUC = 0.93, 0.92 Chen et al [85] 2022 Retrospective NSCLC bone metastases CT 195 AUC = 0.82, 0.73 Filograna et al [86] 2018 Retrospective Vertebral bone marrow metastatic MR 8 T1: AUC = 0.8141 T2: AUC = 0.9116 HOS = high-grade osteosarcoma, STS = soft tissue sarcoma.…”

“…The study concluded that the combination of CT radiomics data and clinical imaging features provided an excellent prediction performance for the occurrence of bone metastasis in patients with non-small cell lung cancer (NSCLC). [85] Radiomics features derived from spinal bone marrow have been found to be associated with the risk of the cancer progression. Filograna et al [86] investigated the hypothesis that quantitative imaging features from preradiotherapy MR images (T1WI and T2WI) could predict the incidence of spinal bone marrow metastasis in cancer patients untreated with radiotherapy.…”

Chest CT radiomics is feasible in evaluating bone changes in chronic kidney diseases

“…The least absolute shrinkage and selection operator (LASSO) regression method is frequently used to select features and establish a model (11). There are many publications reporting the value of LASSO in CT radiomics of bone diseases (12). However, to the best of our knowledge, no study uses CT radiomics to evaluate the change in bone microstructure caused by CKD.…”

Retracted: Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”