Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

Abstract

Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study

“…The region of the BBOX containing both the primary tumor and nearby peritoneum is larger than the ROI of the primary tumor. Recent reports ( 26 , 29 – 32 , 39 , 40 ) have illustrated that peritumoral tissue-based radiomics analysis may reveal valuable information for diagnosis, prognosis and treatment response evaluations. Therefore, in our study, we focused on both the characteristic features of the primary tumor and the peritumoral tissue delineated with the BBOX to develop a radiomics model for the noninvasive diagnosis of OPM.…”

“…The analysis of peritumoral tissues surrounding the tumor mass can reveal important information related to tumor aggressiveness; it can reflect lymphovascular invasion, lymphangiogenesis, and angiogenesis ( 41 – 43 ) and provide other information that can be used for diagnostic and prognostic predictions ( 26 , 29 – 32 , 39 – 42 ). Moreover, such information may be effectively captured by radiomics analysis ( 26 , 29 – 32 , 39 , 40 ).…”

“…The BBOX, which contains both the tumor tissue and peritumoral area (PERI), covers more regions than manual annotation of the tumor alone. A few studies have revealed that the combined radiomic signature of intratumoral and peritumoral regions can provide valuable information for the prediction of the Lauren classification of GC ( 29 ), the pathological complete response to neoadjuvant chemoradiation of esophageal squamous cell carcinoma ( 30 ), and the outcomes and chemotherapy response in GC ( 31 , 32 ).…”

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

“…Using histopathological results as a reference, six studies explored the correlation between AI-based models and prognosis-related factors of tumor differentiation grade ( 9 , 14 , 15 , 25 ), Lauren classification ( 14 , 39 , 47 ), and lymphovascular and neural invasion ( 14 , 17 , 25 , 34 ). Two studies were based on MRI images ( 15 , 17 ) and four were on CT images ( 14 , 25 , 34 , 39 ), and all models exhibited good predictive ability for GC before operation.…”

Gastric adenocarcinoma of the fundic gland: A review of clinicopathological characteristics, treatment and prognosis

“…Morphologically, gastric cancer features a wide range of morphological heterogeneity, resulting in a variety of different histological subtypes. 1 According to the Lauren classification, gastric cancer is classified into intestinal and diffuse types 2 ; according to the Nakamura classification, gastric cancer is grouped into differentiated and undifferentiated types. 3 Based on molecular and genetic profiling, gastric cancer is divided into four genetic subtypes: Ep-steinBarr virus (EBV)infected tumors, microsatellite instability (MSI) tumors, genomically stable tumors, and chromosomally unstable tumors.…”