A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma

Abstract

Objective This study aimed to evaluate the role of tumor and mini-peritumor in the context of CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC). Methods A total of 58 fp-AMLs and 172 ccRCCs were enrolled. The volume of interest (VOI) was manually delineated in the standardized CT images and radiomics features were automatically calculated with software. After methods of feature selection, the CT-based logistic models including tumoral model (Ra-tumor), mini-peritumoral model (Ra-peritumor), perirenal model (Ra-Pr), perifat model (Ra-Pf), and tumoral+perirenal model (Ra-tumor+Pr) were constructed. The area under curves (AUCs) were calculated by DeLong test to evaluate the efficiency of logistic models. Results The AUCs of Ra-peritumor of nephrographic phase (NP) were slightly higher than those of corticomedullary phase (CMP). Furthermore, the Ra-Pr showed significant higher efficiency than the Ra-Pf, and relative more optimal radiomics features were selected in the Ra-Pr than Ra-Pf. The Ra-tumor+Pr combined tumoral and perirenal radiomics analysis was of most significant in distinction compared with Ra-tumor and Ra-peritumor. Conclusion The validity of NP to differentiate fp-AML from ccRCC was slightly higher than that of CMP. To the NP analysis, the Ra-Pr was superior to the Ra-Pf in distinction, and the lesions invaded to the perirenal tissue more severely than to the perifat tissue. It is important to the individual therapeutic surgeries according to the different lesion location. The pooled tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

“…This approach overcame the problem related to the accurate delineation of tumoral volume of interest (VOI). 52 The same authors provided, in addition, a radiomics CT nomogram for discriminating AML from RCC, 53 built using selected features reaching an AUC, for this nomogram, of 0.968. 54 More recently, Han et al performed a retrospective research in 58 patients with AML and 140 with RCC, pathologically confirmed, to evaluate the prognostic value of CT radiomics in distinguishing AML from RCC.…”

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

“…A majority of studies focused on the characterization of solid renal neoplasms (benign vs malignant) using MRI-based quantitative radiomics analyses (e.g., histogram and texture features) (34)(35)(36). However, only a small portion of studies aimed to distinguish subtypes of benign and malignant renal tumors, for instance, mf-AML and ccRCC (37)(38)(39)(40)(41). These studies proposed a CT-based radiomics model for preoperative differentiating mf-AML from homogeneous ccRCC (37)(38)(39)(40)(41).…”

“…However, only a small portion of studies aimed to distinguish subtypes of benign and malignant renal tumors, for instance, mf-AML and ccRCC (37)(38)(39)(40)(41). These studies proposed a CT-based radiomics model for preoperative differentiating mf-AML from homogeneous ccRCC (37)(38)(39)(40)(41). Ma et al observed that mini-peritumoral and perirenal radiomics features contributed to the differentiation of mf-AML from ccRCC (38).…”

“…These studies proposed a CT-based radiomics model for preoperative differentiating mf-AML from homogeneous ccRCC (37)(38)(39)(40)(41). Ma et al observed that mini-peritumoral and perirenal radiomics features contributed to the differentiation of mf-AML from ccRCC (38). We chose MRI instead of CT because the former provides multi-parametric sequences, which theoretically provides information than simple attenuation differences measured in Hounsfield units on CT (42).…”

Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics

“…A meta-analysis of adherent perinephric fat concluded that it leads to an increased blood loss and a prolonged operating time (6). It has been reported that the radiomics model combining tumoral and peritumoral radiomics features was of most significance in distinguishing renal angiomyolipoma and ccRCC (7). Therefore, understanding its tumoral and peritumoral peculiarity such as perirenal fat thickness (8) has assumed great significance in clinical and pathological research, as well as in treatment decisions (2).…”