Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study

Abstract

The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. Methods: We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. Results: The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intraperitumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable. Conclusions: Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.

Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study

“…For the test cohort, the sensitivity and specificity of the models built with only deep learning features were usually low, which Therefore, the proportion of radiomics features in the combined model was high, and the result was good. In most of our models, the performance of either the deep learning model or the radiomics model was insufficient, making it difficult to improve the performance of the combined model, which was consistent with the conclusions of previous research (39).…”

Distinguishing brain abscess from necrotic glioblastoma using MRI-based intranodular radiomic features and peritumoral edema/tumor volume ratio

“…As reported in prior literature, most 11 parameters were significantly different when different ROIs were selected in the same disease [25][26][27][28]. The diagnosis and prediction values of ROIs focusing on intratumoral and peritumoral radiomic features have been compared in several studies on various tumors such as esophageal squamous cell carcinoma, glioma, breast cancer and gastrointestinal stromal tumor [11][12][13][14]. Combinations of intratumoral and peritumoral regions were reported to achieve significantly better performance in radiomics models [11][12][13][14].…”

“…The diagnosis and prediction values of ROIs focusing on intratumoral and peritumoral radiomic features have been compared in several studies on various tumors such as esophageal squamous cell carcinoma, glioma, breast cancer and gastrointestinal stromal tumor [11][12][13][14]. Combinations of intratumoral and peritumoral regions were reported to achieve significantly better performance in radiomics models [11][12][13][14]. Sun et al [11] reported that models based on peritumor regions were better behaved than those based on intratumor regions, while those that employed combined regions behaved the best.…”

“…Nevertheless, it is noteworthy that the algorithms used and the definitions of the peritumoral region have significant influence on the interpretation of the peritumor region [14]. Three ROIs were compared using both Logistic Regression and Random Forest classifiers.…”

“…However, to the best of our knowledge, there are no studies to date attempting to use radiomic features to distinguish brain abscesses from necrotic GBM. regions) may yield different diagnostic information [11][12][13][14]. Considering the invasive characteristics of GBM, it is reasonable to hypothesize that the region surrounding the tumor's enhanced rim on MRI, which seems to be normal brain tissue to the naked eyes, may contain valuable information for differential diagnosis.…”