The objective of this study is to present an uncommon case of adenomatoid odontogenic tumor (AOT) with an impacted maxillary canine, initially mimicking a follicular cyst. AOT is a rare odontogenic tumor, accounting for approximately 1% to 9% of all odontogenic tumors. It primarily occurs in the maxilla and is often associated with an unerupted permanent tooth. Follicular cysts, also known as dentigerous cysts, are benign odontogenic cysts that encase the crown of an unerupted or impacted tooth. We describe a case of AOT occurring in a 14-year-old male. Both the follicular cyst and adenomatoid odontogenic tumor (AOT) can exhibit similar clinical and radiographic presentations. It is crucial to accurately differentiate between the two to ensure appropriate treatment and prognosis.

Introduction: Colorectal cancer (CRC) is the third most frequent malignancy and the second cause of cancer-related death worldwide. CRC is characterized by morphologic and biological heterogeneity, and molecular profiling is required to select appropriate treatment in the metastatic setting. Mutations in KRAS are detected in approximately 40% of CRCs, with prognostic and predictive value, and with the most frequent being p.G12D. Nonetheless, there are few data on the morphologic features in KRAS-mutated CRCs. Materials and Methods: We retrospectively collected clinicopathological features and molecular profiles of CRCs in a multicenter cohort. Results: A total of 2816 patients from 12 centers were included. KRAS mutation was found in 47.4% of cases; Gly12Asp was detected in 23.9%, with different mutation frequencies between centers. Clinicohistological features associated with Gly12Asp mutation included younger patient age (≤70 years of age), higher prevalence in males (58.6%), NOS histotype (87.1%), low pathologic grade (73.9%), high grade budding—Bd3 (43.8%), and tumoral lympho-vascular invasion (68.9%). Conclusions: Recent data have pinpointed the prognostic and predictive value of Gly12Asp mutation, and our results contribute to understanding its biology, with particular focus on peculiar clinicopathological features. Moreover, we found significant differences in pathology reports and assays for molecular profiling in different centers, which can affect a standardized therapeutic approach in CRC.

Background/Objectives. Radiomics is an emerging imaging-based tool that enhances lesion characterization beyond conventional diagnostic approaches. Its potential in evaluating osteolytic lesions of the jaws lies in improving discrimination between benign and malignant entities. This study aimed at developing a predictive model to identify radiomic features capable of distinguishing benign from malignant lesions. Methods. Subjects with preoperative CT or CBCT and histopathological confirmation were included. A pilot cohort was used for feature selection via LASSO regression, which ranked features by frequency and absolute coefficient. Malignancy was coded as class 1, benign lesions as class 0. Positive coefficients indicated association with malignancy, while negative coefficients with benign characteristics. The most stable features were initially trained on the pilot cohort and then validated on an independent test set through machine learning classifiers as LASSO, support vector machine, artificial neural network, random forest e XGboost. Results. The sample comprised 69 subjects (pilot cohort = 57, test cohort = 12). The predictors selected from LASSO regression were: DifferenceEntropy_GLCM (−0.768), CenterOfMassShift_MORPHOLOGICAL (−1.390), INTENSITY-HISTOGRAM_MaximumHistogramGradientGrayLevel (1.139), GLRLM_ShortRunLowGrayLevelEmphasis (−0.742), and Maximum3DDiameter_MORPHOLOGICAL (0.932). As for model performance on test, LASSO achieved the best performance (AUC 0.83), with perfect specificity and sensitivity of 0.71. SVM showed good AUC but poor sensitivity, while random forest and XGBoost performed poorly (AUC 0.57 and 0.37, respectively). Conclusions. The LASSO model proved to be a transparent and robust classifier, suitable for both feature selection and external validation. The selected features demonstrated strong discriminative ability, supporting the potential of radiomics in improving lesion assessment and guiding clinical decision-making.

The expansion of neoadjuvant immune checkpoint blockade, chemoimmunotherapy, and targeted therapies in oncogene-driven tumors is reshaping the management of resectable non-small cell lung cancer (NSCLC). Major pathologic response (MPR) and pathologic complete response (pCR), defined respectively as 10% or less residual viable tumor (RVT) within the primary tumor bed and the complete absence of viable tumor in both the resected primary tumor and sampled regional lymph nodes, have become widely adopted early efficacy endpoints. Both are consistently associated with favorable survival outcomes at the patient level, although their validity as trial-level surrogates for long-term outcomes remains incompletely established. Accurate pathologic response assessment requires rigorous standardization of gross specimen handling, tumor bed sampling, and microscopic quantification of viable tumor, necrosis, and stroma. International recommendations have improved methodological harmonization, and reproducibility studies have demonstrated good interobserver agreement when standardized protocols are applied. Increasing evidence also indicates that RVT behaves as a continuous prognostic variable and that integration of primary-tumor and nodal response may improve postoperative risk stratification. Beyond quantification of RVT alone, additional histologic characteristics of the residual tumor, together with stromal and immune therapy-related features, may provide complementary prognostic information. Digital pathology and artificial intelligence may support more reproducible quantitative assessment, whereas circulating tumor DNA-based molecular residual disease evaluation may capture systemic risk not represented by the resection specimen. These emerging approaches remain investigational and require prospective validation. This review critically examines the methodological standardization, biological interpretation, and prognostic validation of pathologic response in resectable NSCLC, and discusses future strategies integrating histopathologic, digital, and molecular variables into post-neoadjuvant risk assessment.