Clinicopathological Characteristics, Survival Outcomes, and Prognostic Factors in Adult Patients With Sinonasal Rhabdomyosarcoma: Insights from a Retrospective Cohort Study.

BACKGROUND/AIM: Rhabdomyosarcoma (RMS) is a rare tumor with distinct morphological types and challenging diagnosis. This study aimed to investigate clinicopathological characteristics, survival outcomes, and factors influencing prognosis in adult patients with sinonasal RMS, addressing a critical gap in knowledge. PATIENTS AND METHODS: This retrospective cohort study employed various statistical analyses to investigate patients with RMS. Descriptive statistics summarized demographic and clinical characteristics, while survival analysis using the Kaplan-Meier method and Cox proportional hazards model explored the relationship between covariates and survival outcomes. RESULTS: We analyzed 13 cases (7 males, 6 females) of sinonasal RMS. The average age at onset was 42.5 years (standard deviation 18.9). Tumors were observed in multiple locations, predominantly in the maxillary sinus (n=7), followed by the ethmoid sinus (n=5), and the sphenoid sinus (n=1). The study revealed a low survival rate, with 12 patients succumbing to the disease and only one patient surviving. Over time, survival probabilities declined from 92.31% (at 0.5 months) to 7.69% (at 45 months). The analysis indicated a borderline statistically significant positive association between age at diagnosis below 40 years and survival (p=0.05). Sex was found to be significantly associated with survival (p=0.03), with male patients exhibiting a higher survival rate (hazard ratio=0.08, 95%CI=0.01-0.81). CONCLUSION: This study highlights the complex nature of sinonasal RMS in adults. The low survival rate and distinct tumor locations emphasize the need for further research to improve diagnosis and treatment outcomes.

Prognostic Implications of PRAME Expression and Clinicopathological Factors in Sinonasal Mucosal Melanoma: A Single-center Cohort Study of 30 Cases.

BACKGROUND/AIM: Mucosal melanoma (MM) is a rare, aggressive neoplasm in the head and neck region, primarily affecting the nasal cavity and maxillary sinus. This study investigates the correlation of PRAME IHC staining with clinicopathological factors and survival outcomes in sinonasal MM. PATIENTS AND METHODS: The retrospective cohort included patients diagnosed with melanoma from January 2011 to May 2022. Histopathological reassessment confirmed MM subtype (epithelioid or spindle). IHC testing involved S100, MelanA, HMB45, SOX10, PRAME, BRAF V600E, and pan-TRK. Kaplan-Meier and Cox proportional hazards analyses explored survival probabilities and outcomes. RESULTS: The cohort comprised 30 patients (17 females, 13 males) with a mean age of 65 years (standard deviation 13). The 5-year survival probability was approximately 32%. Cox analysis revealed male sex and PRAME IHC staining in ≥70% of cells as associated with lower survival probability. CONCLUSION: In sinonasal MM, PRAME IHC staining in ≥70% of cells is associated with significantly lower survival probability. Male sex, pN1 stage, and tumor location in the sinus are also predictive of poorer survival outcomes. These findings highlight the potential prognostic significance of PRAME expression and other clinicopathological factors in MM. Further studies are warranted to validate and expand upon these observations.

A systematic review of global legal regulations on the permissible level of heavy metals in cosmetics with particular emphasis on skin lightening products.

BACKGROUND: There is an urgent need to address the safety problems caused by the use of skin lightening cosmetics. Evidence suggests that some of them may contain heavy metals. OBJECTIVES: We conducted a systematic review of global legal regulations regarding the permissible level of mercury, lead, arsenic, and cadmium in cosmetic products, with particular emphasis on skin lightening preparations. METHODS: The systematic search of documents was a two-stage process. First, official websites of 17 regional organizations and subsequently regulations for countries with a population over 100 million were searched. RESULTS: Fifteen legislative acts, encompassing more than 67·2% of the global population were reviewed. Regulations were identified for 44/59 high income countries, 16/55 upper middle income countries, 9/45 lower income countries, 0/34 low income countries. The median adult literacy rate was 91·4% and 64·2% in countries with and without regulations, respectively. The use of mercury, lead, arsenic, and cadmium has been banned in 67, 67, 65, and 65 out of 69 countries, respectively. CONCLUSIONS: While regulations exist in most of the high income countries, in low income countries there is a lack of similar standards. In most countries for which these legal regulations have been identified, restrictions on the permissible level of heavy metals are strict. There is a need for enforcement of existing rules, and rigorous assessment of the effectiveness of these regulations.

Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol.

Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients' CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium.

AIMS: A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. METHODS: For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. DATASET AND RESULTS: The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. CONCLUSIONS: Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors' accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f.

A semi-automatic segmentation method for the structural analysis of carotid atherosclerotic plaques by computed tomography angiography.

AIM: Computed tomography angiography (CTA) is currently the most reliable imaging technique for evaluating and planning the treatment of atherosclerosis. The drawbacks of the technique are its low spatial resolution and challenging manual measurements. The purpose of this study was to develop a semi-automatic method to segment vessel walls, surrounding tissue, and the carotid artery lumen to measure the severity of stenosis. METHODS: In vivo contrast CTA images from eight patients undergoing endarterectomy were analyzed using a tailored five-step process involving an adaptive segmentation algorithm and region growing to measure the maximum percent stenosis in the cross-sectional area of the carotid artery. The accuracy of this method was compared with that of manual measurements made by physicians. RESULTS: There were no significant differences between the maximum percent stenosis value obtained using the semi-automatic tool and that obtained using manual measurements (6%; p=0.31). The data acquisition and analysis required an average of 145 seconds. CONCLUSION: This new semi-automatic segmentation method for CTA provides a fast and reliable tool to quantify the severity of carotid artery stenosis.