Respiratory papilloma is a relatively common benign tumor of the respiratory tract, and a few patients may develop malignant changes. The disease has an insidious onset and lacks specific clinical manifestations, and its manifestations are closely related to the growth mode, location and size of the tumor. It can involve multiple parts, such as the larynx, trachea, bronchus, and lung parenchyma, which cause coughing, hoarseness, dysphonia, and, in severe cases, may lead to obstruction of the respiratory tract. At present, the treatment of respiratory papilloma lacks standardization, and there is no effective method to cure the disease. Surgery remains the main treatment for alleviating patients\' symptoms and preventing airway obstruction. However, due to the high recurrence rate of respiratory papilloma, multiple surgeries are often needed, which reduces the quality of life of patients and increases their disease burden and economic burden. Bevacizumab, a vascular endothelial growth factor-binding antibody inhibitor, is a promising adjuvant treatment modality that shows good potential for reducing symptoms and the frequency of surgery. This article aimed to review the efficacy and safety of bevacizumab for the treatment of respiratory papilloma and discuss the differences and efficacy of the systemic application and intralesional injection of bevacizumab for the treatment of respiratory papilloma.
呼吸道乳头状瘤是呼吸道较常见的良性肿瘤，少数可发生恶变。该病起病隐匿，缺乏特异性临床表现，其表现与肿瘤生长方式、部位、大小密切相关，可有喉、气管、支气管、肺实质等多个部位受累，引起咳嗽、声音嘶哑、发音困难，严重者可致呼吸道梗阻。目前，呼吸道乳头状瘤的治疗缺乏规范统一的标准，且尚无治愈该病的有效方法，手术是减轻患者症状、预防气道梗阻的主要治疗方式。然而呼吸道乳头状瘤复发率高，患者往往需要经历多次手术治疗，频繁的手术降低患者的生活质量，增加患者的疾病负担与经济负担。贝伐珠单抗作为血管内皮生长因子结合抗体抑制剂，是一种有希望的辅助治疗方式，在减轻症状、减少手术频率方面表现出较好的潜力。本文主要对贝伐珠单抗治疗呼吸道乳头状瘤的有效性及安全性进行综述，并探讨全身性应用和病灶内注射贝伐珠单抗治疗呼吸道乳头状瘤的差异及疗效。.

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.

A mediastinal cavernous hemangioma is difficult to distinguish from other types of mediastinal tumours. They are usually asymptomatic and incidentally discovered in an imaging study but can present with compressive symptoms or by infiltration of adjacent structures. A 64-year-old woman with a prior history of triple negative invasive carcinoma of the breast, under surveillance was referred after a Chest CT-scan showed a soft tissue 40x20 mm mediastinal mass, suggestive of a thymoma, and as such no tissue biopsy was obtained. A right-side uniportal VATS was performed, the anterior mediastinum dissected and the mass was exposed, and several anomalous veins were identified. Histopathology showed 36x31x15 mm mass, compatible with a cavernous hemangioma of the anterior mediastinum. This case, whilst not questioning the NCCN statement suggesting not doing a tissue biopsy, points to the fact that rare differential diagnosis, like a Cavernous Hemangioma do exist, and a careful and sound judgement is needed at all times.

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OBJECTIVE: To investigate the feasibility of a radiomics nomogram model for predicting malignant transformation in sinonasal inverted papilloma (IP) based on radiomic signature and clinical risk factors.
METHODS: This single institutional retrospective review included a total of 143 patients with IP and 75 patients with IP with malignant transformation to squamous cell carcinoma (IP-SCC). All patients underwent surgical pathology and had preoperative magnetic resonance imaging (MRI) and computed tomography (CT) sinus studies between June 2014 and February 2022. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI), T2-weighted images (T2WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) were performed to select the features extracted from the sequences mentioned above. Independent clinical risk factors were identified by multivariate logistic regression analysis. Radiomics nomogram was constructed by incorporating independent clinical risk factors and radiomics signature. Based on discrimination and calibration, the diagnostic performance of the nomogram was evaluated.
RESULTS: Twelve radiomics features were selected to develop the radiomics model with an area under the curve (AUC) of 0.987 and 0.989, respectively. Epistaxis (p=0.011), T2 equal signal (p=0.003), extranasal invasion (p<0.001), and loss of convoluted cerebriform pattern (p=0.002) were identified as independent clinical predictors. The radiomics nomogram model showed excellent calibration and discrimination (AUC: 0.993, 95% confidence interval [CI]: 0.985-1.00 and 0.990, 95% CI: 0.974-1.00) in the training and validation sets, respectively.
CONCLUSIONS: The nomogram that the combined radiomics signature and clinical risk factors showed a satisfactory ability to predict IP-SCC.

BACKGROUND: Inverted papillomas (IP) are benign epithelial tumors with a tendency to be locally invasive and with disposition to recur. The aim of our study is to present the results of IP treatment, considering pathological, immunohistochemical and molecular features of recurrence.
METHODS: From 1978 to 2020, 186 sinonasal IPs surgeries corresponding to 152 patients were treated in our center. We performed a pathology evaluation of all the recurrent cases reviewing the histological diagnosis, the presence of mixed component other than IP, the koilocytic changes, the p16 over expression and HPV-DNA detection.
RESULTS: Overall recurrence rate was 19 % (35/186). The 35 IP recurrences correspond to 22 patients, 9 of whom presented a single recurrence (single recurrence group) while 13 of them presented more than one recurrence (multi-recurrent group). Immunohistochemical analysis showed a higher percentage of p16 overexpression (54 % vs 33 % p = 0.415) and HPV-DNA presence (23 % vs 0 % p = 0.240) in the multi-recurrent group compared with single recurrence group. In addition, the revision showed more IP with exophytic papilloma focus (38 vs 22 % p = 0.648) and a higher proportion of IP with koilocytotic changes (61 % vs 22 % p = 0.099) in the multirecurrent group. There is no significant difference between groups in our results.
CONCLUSIONS: The analysis of our patients may differentiate between two groups with recurrent papillomas. A single recurrence group where the cause of recurrence is probably an anatomical problem related to an incomplete resection, and a second pattern, the multi-recurrence group, where HPV infection may be the main cause of recurrence.

OBJECTIVE: The variation in mortality rates of intensive care unit oncological patients may imply that clinical characteristics and prognoses are very different between specific subsets of patients with cancer. The specific characteristics of patients with cancer have not been included as risk factors in the established severity-of-illness scoring systems and comorbidity scores, showing limitations in predicting mortality risk. This study aimed to devise a predictive tool for in-hospital mortality for adult patients with a respiratory neoplasm admitted to the intensive care unit, using an artificial neural network.
METHODS: A total of 1,221 stays in the intensive care unit from the Beth Israel Deaconess Medical Center were studied. The primary endpoint was the all-cause in-hospital mortality prediction. An artificial neural network was developed and compared with six severity-of-illness scores and one comorbidity score. Model building was based on important predictors of lung cancer mortality, such as several laboratory parameters, demographic parameters, organ-supporting treatments, and other clinical information. Discrimination and calibration were assessed.
RESULTS: The AUROC for the multilayer perceptron was 0.885, while it was <0.74 for the conventional systems. The AUPRC for the multilayer perceptron was 0.731, whereas it was ≤0.482 for the conventional systems. The superiority of multilayer perceptron was statistically significant for all pairwise AUROC and AUPRC comparisons. The Brier Score was better for the multilayer perceptron (0.109) than for OASIS (0.148), SAPS III (0.163), and SAPS II (0.154).
CONCLUSIONS: Discrimination was excellent for multilayer perceptron, which may be a valuable tool for assessing critically ill patients with lung cancer.

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The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.