### 1.1 Image Processing and Analysis for Preclinical and Clinical Applications
#### 1.1.8 Principal and Clinical Applications
[10] to detect traumatic fractures in patients. A convolutional neural network pre-trained on non-medical images was re-trained and fine-tuned using 148 CT images to produce a model for the classification of future CTs as fracture or not fracture. The validation and test datasets were characterized by 30 patients: both datasets contained 5 patients without fractures and 25 with fractures. The results showed an accuracy of 80% in classifying the maxillofacial fractures. Consequently, the proposed model can be used as a care support, reducing the risk of human error, preventing patient harm by minimizing diagnostic delays, and reducing the incongruuous burden of hospitalization. Among the other research papers, three of them focus on radiomics applications, including restaging in metastatic colorectal cancer [12], evaluating the robustness of PET radiomics features after MRI co-registration [13], and predicting pathologic complete response after neoadjuvant chemoradiation therapy for rectal cancer [14]. [PERSON] et al. [12] investigated the application of [18F]FDG PET/CT image-based textural features analysis to early predict disease progression and survival outcome in 52 metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, radiomics features from PET and low-dose CT images were extracted. The hybrid descriptive-inferential method [15] was used for feature selection while the discriminant analysis [16] was used for the predictive model implementation. The prediction performance was evaluated for per-lesion analysis, per-patient analysis, and per liver lesions analysis. All results showed that the proposed radiomics model was feasible and potentially useful in the predictive evaluation of disease progression in MCC. [PERSON] et al. [13] studied the variability in PET radiomics features under the impact of co-registration with MRI using the difference percentage coefficient and the Spearman's correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI, and (iii) PET after co-registration with FLAIR MRI. For this purpose, 77 patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features, and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using Spearman's correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction. [PERSON] et al. [14] evaluated the MRI assessment after neoadjuvant chemoradiotherapy (nCRT) in 912 patients with rectal cancer for staging and treatment planning purposes. They proposed a pathologic complete response (pCR) prediction method based on a novel multi-parametric MRI embedding technique. Specifically, multiple MRI sequences were encapsulated into multi-sequence fusion images (MSFI). Subsequently, radiomics features were extracted and used to predict pCR through a random forest classifier. The results demonstrated that the use of all given MRI sequences is the most effective method regardless of the dimension reduction method. Furthermore, it outperformed four competing baselines in terms of the area under the receiver operating characteristic curve (AUC) and F1-score.