Salivary gland mucoepidermoid carcinoma is a clinically, morphologically and genetically heterogeneous entity: a clinicopathological study of 40 cases with emphasis on grading, histological variants and presence of the t(11;19) translocation

International audienceAims: To correlate World Health Organization (WHO) grade, patient's outcome and presence of t(11;19) to histological tumor variants in 40 well characterized mucoepidermoid carcinomas (MECs) out of a series of 290 salivary gland carcinomas. Methods and Results: MECs were classified as classical based on the presence of equal proportions of the three cell types or the dominance (≥50%) of mucous cells beside at least one other cell type, and as variant if composed of ≥80% single cell type. Classical MECs were more common (n=23). Variant MECs had predominant squamoid (n=9), eosinophilic (n=5), or clear cell (n=3) morphology. 27 tumors were WHO grade 1, 3 grade 2 and 10 grade 3. The t(11;19) was detected in 82%, 35% and 0% of classical MEC, variant MEC and non-MEC, respectively. Classical MECs were significantly associated with age ≤60 years (p<0.001), grade 1 (p<0.001), and t(11;19) (p=0.003). Short overall survival was significantly associated with age >60 years (p=0.001) and UICC stage >I (p=0.031), residual tumor (p<0.001), tumor grade >1 (p=0.001) and squamoid variant (p=0.002) in Kaplan-Meier analysis. Conclusions: The results underscore the great histological diversity of MEC, the reproducibility of the WHO grading and the value of histological subtypes as an additional prognostic factor

Multicenter Observational Study to Evaluate the Diagnostic Value of Sonography in Patients with Chronic Rhinosinusitis

(1) Background: Computed tomography (CT) is considered mandatory for assessing the extent of pathologies in the paranasal sinuses (PNS) in chronic rhinosinusitis (CRS). However, there are few evidence-based data on the value of ultrasound (US) in CRS. This multicenter approach aimed to compare diagnostic imaging modalities in relation to findings during surgery. (2) Methods: 127 patients with CRS were included in this prospective multicenter study. Patients received preoperative US and CT scans. The sensitivity and specificity of CT and US were extrapolated from intraoperative data. (3) Results: CT scans showed the highest sensitivity (97%) and specificity (67%) in assessing CRS. Sensitivities of B-scan US were significantly lower regarding the maxillary sinus (88%), the ethmoid sinus (53%), and the frontal sinus (45%). The highest overall sensitivity was observed for assessing the pathology of the maxillary sinus. (4) Conclusions: We observed high accuracy with CT, confirming its importance in preoperative imaging in CRS. Despite the high US expertise of all investigators and a standardized examination protocol, the validity of CT was significantly higher than US. Ultrasound of the PNS sinuses is applicable in everyday clinical practice but lacks diagnostic accuracy. Nevertheless, it might serve as a complementary hands-on screening tool to directly correlate the clinical findings in patients with PNS disease

Multicenter observational study to evaluate the diagnostic value of sonography in patients with chronic rhinosinusitis

(1) Background: Computed tomography (CT) is considered mandatory for assessing the extent of pathologies in the paranasal sinuses (PNS) in chronic rhinosinusitis (CRS). However, there are few evidence-based data on the value of ultrasound (US) in CRS. This multicenter approach aimed to compare diagnostic imaging modalities in relation to findings during surgery. (2) Methods: 127 patients with CRS were included in this prospective multicenter study. Patients received preoperative US and CT scans. The sensitivity and specificity of CT and US were extrapolated from intraoperative data. (3) Results: CT scans showed the highest sensitivity (97%) and specificity (67%) in assessing CRS. Sensitivities of B-scan US were significantly lower regarding the maxillary sinus (88%), the ethmoid sinus (53%), and the frontal sinus (45%). The highest overall sensitivity was observed for assessing the pathology of the maxillary sinus. (4) Conclusions: We observed high accuracy with CT, confirming its importance in preoperative imaging in CRS. Despite the high US expertise of all investigators and a standardized examination protocol, the validity of CT was significantly higher than US. Ultrasound of the PNS sinuses is applicable in everyday clinical practice but lacks diagnostic accuracy. Nevertheless, it might serve as a complementary hands-on screening tool to directly correlate the clinical findings in patients with PNS disease

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing