Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features

Abstract

Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment 18 F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.Methods: 52 patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumourtumor regression grade (TRG) as follows: 22 as pR+ (9 with TRG=1, 13 with TRG=2) and 30 as pR-(16 with TRG=3, 13 with TRG=4 and 1 with TRG=5). First order parameters and 21 second order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semiautomatically segmented tumourtumors on T2-w MRI, ADC maps and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. Results:In the mono-parametric approach PET homogeneity reached the maximum AUC (0.77; sensitivity=72.7% and specificity=76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing 6 second-order texture features (five from PET and one from T2-w MRI) yields the highest predictivity in distinguish between pR+ and pR-patients (AUC=0.86; sensitivity=86% and specificity=83% at the Youden index). Conclusions:If preliminary results of this study arewill be confirmed, pretreatment PET and MRI images could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.

Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer

“…Radiomics involves high-throughput extraction of large amounts of imaging features from radiologic images and has recently emerged as a promising tool for predicting the prognosis and guidance of therapy [3][4][5]. Moreover, imaging features identified by magnetic resonance (MR)-based radiomics analysis were demonstrated to effectively predict the response of rectal cancer to chemoradiotherapy [4,[6][7][8][9][10]. Radiogenomics is an emerging field that integrates imaging and genomics data to identify imaging correlates of a specific tumor genotype or molecular phenotype for precision medicine [5].…”

Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy

“…The application of radiomics has been extensively studied in esophageal cancer [24], non-small cell lung cancer [25], breast cancer [26], nasopharyngeal carcinoma [27], and rectal cancer [28], which indicates the potential of radiomics for predicting the e cacy of treatment or patient prognosis. Radiotherapyorientated CT imaging must be acquired prior to SBRT treatment of HCC with PVTT.…”

Artificial intelligence in radiation oncology treatment planning: a brief overview

“…Radiomics features are a promising additional data type for oncologic outcome prediction and tumor control probability models (76). Multiple manuscripts have been published using radiomics to predict radiation response, in some cases with prediction power outperforming standard clinical variables (77)(78)(79)(80)(81)(82), though not in all (83). Radiomicsbased statistical approaches can predict various radiation normal tissue complication probabilities including radiation pneumonitis, xerostomia, and rectal wall toxicity (84)(85)(86)(87)(88)(89).…”