Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension—together with shape-based and intensity-based features—were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78–0.92) and 0.81 (0.64–0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57–0.78) and 0.69 (0.51–0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Background. The acinic cell carcinoma (AciCC) of the parotid gland is a rare tumor with an indolent behavior; however, a subgroup of this tumor presents an aggressive behavior with a tendency to recur. The aim of this multicenter study was to identify and stratify those patients with AciCC at high risk of tumor recurrence. Methods. A retrospective study was carried out involving 77 patients treated with surgery between January 2000 and September 2022, in different Italian referral centers. Data about tumor characteristics and its recurrence were collected. The histological specimens and slides were independently reviewed by a senior pathologist coordinator (L.C.) and the institution’s local head and neck pathologist. Results. The patients’ age average was 53.6 years, with a female prevalence in the group. The mean follow-up was 67.4 months (1-258, SD 59.39). The five-year overall survival (OS) was 83.2%. The 5-year disease-free survival (DFS) was 60% (95% CI 58.2–61.7). A high incidence of necrosis, extraglandular spread, lymphovascular invasion (LVI), atypical mitosis, and cellular pleomorphism was observed in the high-risk tumors compared to the low-risk ones. Conclusion. AciCC generally had an indolent behavior, optimal OS, DFS with few cervical node metastases, and rare distant relapses. This multicenter retrospective case series provides evidence of the need for clinical–epidemiological–histological stratification for patients at risk of poor outcomes. Our results suggest that the correct definition of high-risk AciCC should include tumor size, the presence of necrosis, extraglandular spread, LVI, atypical mitosis, and cellular pleomorphism.

Despite recent advances in prevention, detection and treatment, oral squamous cell carcinoma (OSCC) remains a global health concern, strongly associated with environmental and lifestyle risk factors and infection with oncogenic viruses. Merkel Cell Polyomavirus (MCPyV), well known to be the causative agent of Merkel Cell Carcinoma (MCC) has been found in OSCC, suggesting its potential role as a co-factor in the development of oral cavity cancers. To improve our understanding about MCPyV in oral cavities, the detection and analysis of MCPyV DNA, transcripts and miRNA were performed on OSCCs and oral potentially malignant disorders (OPMDs). In addition, the cellular miR-375, known to be deregulated in tumors, was examined. MCPyV DNA was found in 3 out of 11 OSCC and 4 out of 12 OPMD samples, with a viral mean value of 1.49 × 10² copies/mL. Viral integration was not observed and LTAg and VP1 transcripts were detected. Viral miRNAs were not detected whereas the cellular miR-375 was found over expressed in all MCPyV positive oral specimens. Our results reported evidence of MCPyV replication in both OSCC and OPMD suggesting the oral cavity as a site of replicative MCPyV infection, therefore underscoring an active role of this virus in the occurrence of oral lesions.

Hypercalcitoninaemia has been described in patients with pseudohypoparathyroidism (PHP) type 1A and 1B. Elevated calcitonin levels are thought to result from impaired Gsα receptor signaling, leading to multiple hormone resistance. Evidence on the risk of medullary thyroid carcinoma (MTC) or C-cell hyperplasia in PHP patients with hypercalcitoninaemia is lacking. A 43-year-old Caucasian man was referred to our endocrinology clinic for chronic hypocalcemia associated with elevated serum parathormone levels and a single cystic thyroid nodule. The patient did not show skeletal deformities, and screening for concomitant hormone resistances was negative, except for the presence of elevated serum calcitonin levels. The workup led to a molecular diagnosis of sporadic PHP1B. Fine needle aspiration of the thyroid nodule was not diagnostic. The calcium stimulation test yielded an abnormal calcitonin response. Given the scarcity of data on the risk of thyroid malignancy in PHP and calcium stimulation test results, total thyroidectomy was performed. Histological examination revealed cystic papillary thyroid cancer in a background of diffuse C-cell hyperplasia. To our knowledge, we are the first to describe a rare form of thyroid cancer combined with C-cell hyperplasia in a patient with PHP and hypercalcitoninaemia. In the present case, a mere receptor resistance might not fully explain the elevated calcitonin levels, suggesting that hypercalcitoninaemia should be carefully evaluated in PHP patients, especially in the case of concomitant thyroid nodules. Further studies on larger cohorts are needed to elucidate this topic.

The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation. We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements. Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01). We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies.

Introduction: The aim of this project is to assess interobserver agreement for programmed death‐ligand 1 (PD‐L1) scoring on of non–small cell lung cancer (NSCLC) on cytological specimens in a large‐scale multicenter study, by exploiting the cell block‐derived tissue microarray (cbTMA) approach.Methods: A total of 65 cell blocks (CB) diagnosed as NSCLC were retrospectively collected and selected for TMA preparation. Hematoxylin–eosin and PD‐L1 stained slides were digitized and uploaded on a free web sharing platform. Participants were asked to provide PD‐L1 assessment by using the clinically relevant cutoff of tumor proportion score (TPS) (50%). Interobserver agreement was calculated using Fleiss’s κ.Results: Of 65 CBs, 11 were deemed not suitable; therefore, an overall number of 54 cores were used for the preparation of four TMAs. A total of 1674 evaluations were provided by 31 cytopathologists from 21 different institutions in nine countries. The statistical analysis showed a moderate overall agreement (κ = 0.49). The highest agreement was achieved in the TPS >50% category (κ = 0.57); moderate agreement was observed in TPS <1% category (κ = 0.51) and the lowest κ value was obtained for TPS 1%–49% category (k = 0.32).Conclusions: The overall moderate agreement observed showed that there is still room for improvement in inter‐pathologist agreement for PD‐L1 evaluation on cytological samples, highlighting the need for standardization in sample preparation, focused training in PD‐L1 evaluation on cytological material, and the integration of machine learning tools to improve interobserver consistency.