## 4 Radiomics and ML in ASCC
### Mri  
Disregarding radiomics, MRI analysis struggles to provide valuable prognostic help [28]. Nonetheless, it is worth noting that the use of MRI-determined tumor regression grading (TRG) to predict local relapse at three and six months post-CRT has been associated with an almost 100% negative predictive value for TRG 1/2 scores at three and six months, and almost 100% positive predictive value for TRG 4/5 at six months [29]. Figure 2: The “holistic flower” of decision-making for ASCC. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Number of Patients and** & **Imageability** & **Aain** & **Selected Imaging** & **Prediction Algorithm** & **Main Results** \\  & & **Type of Study** & **and Segmentation** & & **Parameters** & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2018)} & \multirow{4}{*}{Muncovertic, retrospective} & \(n=28\) & Proc/CIT, NST, MRI & \multirow{4}{*}{Predict recurrence or death} & First and second-order & \multirow{4}{*}{Cox regression} & \multirow{4}{*}{Baseline 72\% energy and UV Coefficient of Variation associated with endpoints} \\  & & (axial Test) & & & & & \\  & & (GLCD) BCS & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  &  
Three published studies have explored various complexity levels of MRI radiomics in ASCC. In a monocentric retrospective study, conducted by [PERSON] et al., 28 non-metastatic ASCC patients were included and pre-CRT tumor volumes were manually segmented on axial T2-weighted (T2w) sequences. First-order radiomics features and second-order statistical features derived from the grey-level co-occurrence matrix (GLCM) were performed, using an in-house ITK library-based Python script. Inter-observer variability in manual segmentation was assessed by case-to-case consensus between 2 experts, and all MRI acquisitions were performed on the same 1.5-T machine, thus limiting results extrapolation to other centers. After adjusting for age, gender and tumor grade, two radiomics features were associated with event (disease progression or death) occurrence: skewness (HR = 0.131, \(p\) = 0.005) and cluster shade_d1 (HR = 0.601, \(p\) = 0.027). The corresponding Harrell C-indices were respectively 0.846 and 0.851 [30]. Similarly, in a retrospective study published by [PERSON] et al. in 2019, pre and post-CRT (median of 15 weeks from the start of radiotherapy with 90% of scans performed within 12 weeks of completion of treatment) MRI heterogeneity was evaluated. T2w but also functional diffusion-weighted (DW) imaging sequences were analyzed to predict post-treatment recurrence (locoregional or metastatic). Three 1.5-T MRI machines were used, all with the same acquisition protocol, and 40 patients were included. The tumor volume was manually delineated on all slices of axial T2w and ADC parametric maps on pre and post-CRT acquisitions, generating four separate whole tumor 3D volumes-of-interest (VOI) per case. Tumor maximum size, volume, extent and TNM stage were recorded by a consensus of two experts. Tumor volume and seventy-eight first-order, second-order (based on GLCM statistics) and fractal features were extracted from the VOI using in-house software. A ML random forest method was used to select variables with highest discriminatory value in predicting the two selected outcomes: disease recurrence and two-year disease-free survival (DFS). A baseline multivariate clinical-only model (age, gender, T stage, N stage) and an extended multiparametric model (addition of top performing imaging features) were developed and compared. Two radiomics features were found to be of high informative value: baseline T2w \"energy\" and DWI \"coefficient of variation\" appeared to be predictive of CRT outcome, independently of clinical characteristics alone. The addition of these two imaging features to multivariate logistic regression models based on clinical characteristics yielded an increase in the predictive accuracy for both endpoints when using C-statistic and net reclassification improvement (\(p\) < 0.001) algorithms.