Study rationale and design of the PEOPLHE trial.

PURPOSE: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS: PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION: The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.

Pleural Mesothelioma: Treatable Traits of a Heterogeneous Disease.

Pleural mesothelioma is an aggressive disease with diffuse nature, low median survival, and prolonged latency presenting difficulty in prognosis, diagnosis, and treatment. Here, we review all these aspects to underline the progress being made in its investigation and to emphasize how much work remains to be carried out to improve prognosis and treatment.

Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer.

BACKGROUND AND RATIONALE: The therapeutic interventions against lung cancer are currently based on a fully personalized approach to the disease with considerable improvement of patients' outcome. Alongside continuous scientific progresses and research investments, massive technologic efforts, innovative challenges, and consolidated achievements together with research investments are at the bases of the engineering and manufacturing revolution that allows a significant gain in clinical setting. AIM AND METHODS: The scope of this review is thus to focus, rather than on the biologic traits, on the analysis of the precision sensors and novel generation materials, as semiconductors, which are below the clinical development of personalized diagnosis and treatment. In this perspective, a careful revision and analysis of the state of the art of the literature and experimental knowledge is presented. RESULTS: Novel materials are being used in the development of personalized diagnosis and treatment for lung cancer. Among them, semiconductors are used to analyze volatile cancer compounds and allow early disease diagnosis. Moreover, they can be used to generate MEMS which have found an application in advanced imaging techniques as well as in drug delivery devices. CONCLUSIONS: Overall, these issues represent critical issues only partially known and generally underestimated by the clinical community. These novel micro-technology-based biosensing devices, based on the use of molecules at atomic concentrations, are crucial for clinical innovation since they have allowed the recent significant advances in cancer biology deciphering as well as in disease detection and therapy. There is an urgent need to create a stronger dialogue between technologists, basic researchers, and clinicians to address all scientific and manufacturing efforts towards a real improvement in patients' outcome. Here, great attention is focused on their application against lung cancer, from their exploitations in translational research to their application in diagnosis and treatment development, to ensure early diagnosis and better clinical outcomes.

Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.

Background: Artificial intelligence (AI) has proved to be of great value in diagnosing and managing Sars-Cov-2 infection. ALFABETO (ALL-FAster-BEtter-TOgether) is a tool created to support healthcare professionals in the triage, mainly in optimizing hospital admissions. Methods: The AI was trained during the pandemic's "first wave" (February-April 2020). Our aim was to assess the performance during the "third wave" of the pandemics (February-April 2021) and evaluate its evolution. The neural network proposed behavior (hospitalization vs home care) was compared with what was actually done. If there were discrepancies between ALFABETO's predictions and clinicians' decisions, the disease's progression was monitored. Clinical course was defined as "favorable/mild" if patients could be managed at home or in spoke centers and "unfavorable/severe" if patients need to be managed in a hub center. Results: ALFABETO showed accuracy of 76%, AUROC of 83%; specificity was 78% and recall 74%. ALFABETO also showed high precision (88%). 81 hospitalized patients were incorrectly predicted to be in "home care" class. Among those "home-cared" by the AI and "hospitalized" by the clinicians, 3 out of 4 misclassified patients (76.5%) showed a favorable/mild clinical course. ALFABETO's performance matched the reports in literature. Conclusions: The discrepancies mostly occurred when the AI predicted patients could stay at home but clinicians hospitalized them; these cases could be handled in spoke centers rather than hubs, and the discrepancies may aid clinicians in patient selection. The interaction between AI and human experience has the potential to improve both AI performance and our comprehension of pandemic management.

The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs.

We investigated the association of T1/T2 mapping values with programmed death-ligand 1 protein (PD-L1) expression in lung cancer and their potential in distinguishing between different histological subtypes of non-small cell lung cancers (NSCLCs). Thirty-five patients diagnosed with stage III NSCLC from April 2021 to December 2022 were included. Conventional MRI sequences were acquired with a 1.5 T system. Mean T1 and T2 mapping values were computed for six manually traced ROIs on different areas of the tumor. Data were analyzed through RStudio. Correlation between T1/T2 mapping values and PD-L1 expression was studied with a Wilcoxon-Mann-Whitney test. A Kruskal-Wallis test with a post-hoc Dunn test was used to study the correlation between T1/T2 mapping values and the histological subtypes: squamocellular carcinoma (SCC), adenocarcinoma (ADK), and poorly differentiated NSCLC (PD). There was no statistically significant correlation between T1/T2 mapping values and PD-L1 expression in NSCLC. We found statistically significant differences in T1 mapping values between ADK and SCC for the periphery ROI (p-value 0.004), the core ROI (p-value 0.01), and the whole tumor ROI (p-value 0.02). No differences were found concerning the PD NSCLCs.

CT Scan-Guided Fine Needle Aspiration Cytology for Lung Cancer Diagnosis through the COVID-19 Pandemic: What We Have Learned.

BACKGROUND AND RATIONALE: Novel coronavirus-related disease (COVID-19) has profoundly influenced hospital organization and structures worldwide. In Italy, the Lombardy Region, with almost 17% of the Italian population, rapidly became the most severely affected area since the pandemic beginning. The first and the following COVID-19 surges significantly affected lung cancer diagnosis and subsequent management. Much data have been already published regarding the therapeutic repercussions whereas very few reports have focused on the consequences of the pandemic on diagnostic procedures. METHODS: We, here, would like to analyze data of novel lung cancer diagnosis performed in our Institution in Norther Italy where we faced the earliest and largest outbreaks of COVID-19 in Italy. RESULTS: We discuss, in detail, the strategies developed to perform biopsies and the safe pathways created in emergency settings to protect lung cancer patients in subsequent therapeutic phases. Quite unexpectedly, no significant differences emerged between cases enrolled during the pandemic and those before, and the two populations were homogeneous considering the composition and diagnostic and complication rates. CONCLUSIONS: By pointing out the role of multidisciplinarity in emergency contexts, these data will be of help in the future for designing tailored strategies to manage lung cancer in a real-life setting.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Smoking Habit and Respiratory Function Predict Patients' Outcome after Surgery for Lung Cancer, Irrespective of Histotype and Disease Stage.

BACKGROUND: Growing evidence suggests that sublobar resections offer more favorable outcomes than lobectomy in early-stage lung cancer surgery. However, a percentage of cases that cannot be ignored develops disease recurrence irrespective of the surgery performed with curative intent. The goal of this work is thus to compare different surgical approaches, namely, lobectomy and segmentectomy (typical and atypical) to derive prognostic and predictive markers. PATIENTS AND METHODS: Here we analyzed a cohort of 153 NSCLC patients in clinical stage TNM I who underwent pulmonary resection surgery with a mediastinal hilar lymphadenectomy from January 2017 to December 2021, with an average follow-up of 25.5 months. Partition analysis was also applied to the dataset to detect outcome predictors. RESULTS: The results of this work showed similar OS between lobectomy and typical and atypical segmentectomy for patients with stage I NSCLC. In contrast, lobectomy was associated with a significant improvement in DFS compared with typical segmentectomy in stage IA, while in stage IB and overall, the two treatments were similar. Atypical segmentectomy showed the worst performance, especially in 3-year DFS. Quite unexpectedly, outcome predictor ranking analysis suggests a prominent role of smoking habits and respiratory function, irrespective of the tumor histotype and the patient's gender. CONCLUSIONS: Although the limited follow-up interval cannot allow conclusive remarks about prognosis, the results of this study suggest that both lung volumes and the degree of emphysema-related parenchymal damage are the strongest predictors of poor survival in lung cancer patients. Overall, these data point out that greater attention should be addressed to the therapeutic intervention for co-existing respiratory diseases to obtain optimal control of early lung cancer.

Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal.

OBJECTIVES: To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology. METHODS: A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach's alpha (Cα) correlation coefficient. RESULTS: The final SR form included 118 items (6 in the "Patient Clinical Data" section, 4 in the "Clinical Evaluation" section, 9 in the "Imaging Protocol" section, and 99 in the "Report" section). The experts' overall mean score and sum of scores were 4.77 (range 1-5) and 257.56 (range 206-270) in the first Delphi round, and 4.96 (range 4-5) and 208.44 (range 200-210) in the second round, respectively. In the second Delphi round, the experts' overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87). CONCLUSIONS: Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.

Myxolipoma of hand in a child: case report of a rare tumor.

Lipomatous tumors account for less than 10% of tumors in the pediatric population. Myxolipomas (a subset of lipoma characterised by mature adipose tissue and abundant mucoid substance) are found to be even rarer. There are a few case reports in different body parts like heart, kidney, oral cavity, epiglottis, cervical and mediastinal regions. However, there are no case reports on the involvement of the hands in any age group. High resolution ultrasound is the imaging modality of choice for the initial evaluation of superficial soft tissue tumors, their site, nature and extent. In conjunction with clinical findings and age of presentation, it helps in narrowing down the differential diagnosis and planning the management. Hyperechoic fatty tumors in the pediatric hand are mostly benign and includes lipomas, lipoblastomas and fibrous hamartomas of infancy as the main differentials. A definitive diagnosis is based on a histo-pathological and molecular cytogenetic examination. This article presents a never before reported case of a rare, large, myxolipoma of the hand in a 22-month-old boy.

Managing Malignant Pleural Mesothelioma in the Age of Personalized Medicine: Where Are We and What Is Still Missing?

Malignant Mesothelioma (MM) is an aggressive neoplasm of the pleural mesothelium, less frequently peritoneal and exceptionally of the vaginal tunic of the testicle and pericardium [...].

Correlation between PD-L1 Expression of Non-Small Cell Lung Cancer and Data from IVIM-DWI Acquired during Magnetic Resonance of the Thorax: Preliminary Results.

This study aims to investigate the correlation between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in magnetic resonance imaging (MRI) and programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC). Twenty-one patients diagnosed with stage III NSCLC from April 2021 to April 2022 were included. The tumors were distinguished into two groups: no PD-L1 expression (<1%), and positive PD-L1 expression (≥1%). Conventional MRI and IVIM-DWI sequences were acquired with a 1.5-T system. Both fixed-size ROIs and freehand segmentations of the tumors were evaluated, and the data were analyzed through a software using four different algorithms. The diffusion (D), pseudodiffusion (D*), and perfusion fraction (pf) were obtained. The correlation between IVIM parameters and PD-L1 expression was studied with Pearson correlation coefficient. The Wilcoxon−Mann−Whitney test was used to study IVIM parameter distributions in the two groups. Twelve patients (57%) had PD-L1 ≥1%, and 9 (43%) <1%. There was a statistically significant correlation between D* values and PD-L1 expression in images analyzed with algorithm 0, for fixed-size ROIs (189.2 ± 65.709 µm²/s × 104 in no PD-L1 expression vs. 122.0 ± 31.306 µm²/s × 104 in positive PD-L1 expression, p = 0.008). The values obtained with algorithms 1, 2, and 3 were not significantly different between the groups. The IVIM-DWI MRI parameter D* can reflect PD-L1 expression in NSCLC.

Idiopathic pulmonary fibrosis and lung cancer: targeting the complexity of the pharmacological interconnection.

INTRODUCTION: Many data already suggested that cancer and IPF are underlined by a number of common pathogenic biologic pathways. However, fewer data regards the interconnections, in terms of synergy or increased toxicities, of drugs used in cancer and IPF. Particularly, how the specific therapy influences the concurrent condition and prognostic factors of response in patients with both lung cancer and IPF are far to be clarified. Similarly, identification of features of IPF patients with higher risk of developing pulmonary adverse events when treated with chemotherapy, immune checkpoint inhibitors, TKIs, or radiotherapy is of primary importance in clinical practice. AREAS COVERED: We will discuss the scientific rationale, based on the extensive analysis of literature data, by consulting several databases for combining anticancer and antifibrotic treatments and for the design of novel therapeutic strategies. The role of immunotherapy in cancer aroused in IPF context will be discussed with specific interested, based on the continuously increasing role of immune checkpoint inhibition against lung tumors. EXPERT OPINION: This work will help to improve knowledge, based on a multidisciplinary perspective, on IPF and cancer patients, which identify an unmet clinical need. A better management during each phase of disease progression will require the design innovative trials and the development of new drugs and molecules both in the oncologic and respiratory medicine pipeline.

Feasibility and safety of extended pleurectomy/decortication for malignant pleural mesothelioma. A single group experience.

Surgery is part of a multimodal therapeutic approach to malignant pleural mesothelioma (MPM) although its real beneficial effect is still controversial. The optimal precise sequence of treatments within the trimodality is unclear, and should be decided upon a multidisciplinary consensus for each individual patient. Here, we analyzed the perioperative data of 19 MPM patients who underwent extended pleurectomy/decortication (EPD) with curative intent. The mean age at diagnosis was 67 years; 11 males and eight females. Ten patients were diagnosed with MPM via medical thoracoscopy (MT), and nine via video-assisted thoracoscopic surgery (VATS). The vast majority of cases harbored epitheliod forms. We compared neoadjuvant chemotherapy (NCT) followed by surgery (11 cases) versus surgery followed by adjuvant chemotherapy (ACT, 8 cases) within a 3-year period. All patients had extended pleurectomy/decortication and none had an extended pneumonectomy. Analysis of survival curves suggested that the short-term outcomes are better with upfront EDP followed by ACT if compared to EDP preceded by NCT. Although limited, the data highlighted the safety and feasibility of EPD, with manageable postoperative complications and no major burden for the patients.

Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review.

It is well known that lung cancer relies on a number of genes aberrantly expressed because of somatic lesions. Indeed, the lungs, based on their anatomical features, are organs at a high risk of development of extremely heterogeneous tumors due to the exposure to several environmental toxic agents. In this context, the microbiome identifies the whole assemblage of microorganisms present in the lungs, as well as in distant organs, together with their structural elements and metabolites, which actively interact with normal and transformed cells. A relevant amount of data suggest that the microbiota plays a role not only in cancer disease predisposition and risk but also in its initiation and progression, with an impact on patients' prognosis. Here, we discuss the mechanistic insights of the complex interaction between lung cancer and microbiota as a relevant component of the microenvironment, mainly focusing on novel diagnostic and therapeutic objectives.

A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.

The oncogenic landscape of the idiopathic pulmonary fibrosis: a narrative review.

Background and Objective: Translational research is a source of continuous innovation in medicine, more particularly for clinical research on new treatment modalities in idiopathic pulmonary fibrosis (IPF) patients. However, the heterogeneity of the disease is well recognized, and different pathological and molecular settings have been identified. The molecular mechanisms by which IPF proceeds in time and space remains poorly understood. Although some IPF features are reminiscent of cancer, the dynamics of malignant divergent clonal selective pressure and heterogeneity clearly differ from those occurring in IPF. This is reflected in the absence of patient proper selection and stratification to biological agents (pirfenidone, nintedanib) which limit therapeutic efficacy. Consequently, increased costs are related to the clinical management of advanced IPF patients. Steady collaboration and fluid communication between pneumo-oncologists, radiologists and molecular biologists is a clear priority for the correct interpretation of tests and the definition of effective personalized strategies against this orphan disease. The present work aims at providing the most relevant hints shared by cancer and IPF. Methods: A systematic literature review was performed to identify all relevant data. The examined databases were Scopus, Web of Science, Cochrane, Google Scholar, and PubMed. The last search was run on January 5, 2022. We have primarily conducted separated research for lung cancer, IPF, genetics, epigenetics, surgery in IPF and cancer. Key Content and Findings: The data here presented mainly focus on gene mutations, epigenetics and novel therapeutic approaches. Moreover, epidemiology, prognostic variables and in new treatment strategies adopted in patients with IPF and lung cancer are discussed as well. Conclusions: Overall, the findings of this narrative review will be of help in defining the key molecular features that could applied in IPF setting with promising rationale to improve therapy and to better manage those cases carrying IPF and cancer concomitantly.

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center "Blue Sky Radiomics" study on stage III unresectable NSCLC.

BACKGROUND AND PURPOSE: In the retrospective-prospective multi-center "Blue Sky Radiomics" study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features' reproducibility and apply methods for harmonization. MATERIAL AND METHODS: We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat). RESULTS: We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols. CONCLUSIONS: We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility. ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776 .

Psoriatic arthritis: Ultrasound peculiarities with particular emphasis on enthesitis.

The prevalence of psoriatic arthritis among patients with psoriasis has a marked variability with ethnic and geographic variations. Inflammatory changes associated with psoriatic arthritis include bone erosion, tenosynovitis, and synovial hypertrophy, but enthesitis is considered the hallmark. Both X-ray and magnetic resonance imaging (MRI) are usefull in the diagnosis of psoriatic arthritis, but ultrasonography is the best imaging modality to assess entheses. Ultrasound findings of enthesitis include a loss of the regular fibrillar architecture, hypoechoic thickening, hypervascularization of tendons, ligaments, and joint capsules at their bony attachment, bony changes (including irregularities and erosions). Ultrasound has also proved the ability to detect inflammatory subclinical findings and to be useful in the follow-up of therapies.