Germline Polymorphisms Associated with Overall Survival in Lung Adenocarcinoma: Genome-Wide Analysis.

BACKGROUND/OBJECTIVES: Lung cancer remains a global health concern, with substantial variation in patient survival. Despite advances in detection and treatment, the genetic basis for the divergent outcomes is not understood. We investigated germline polymorphisms that modulate overall survival in 1464 surgically resected lung adenocarcinoma patients. METHODS: A multivariable Cox proportional hazard model was used to assess the association of more than seven million polymorphisms with overall survival at the 60-month follow-up, considering age, sex, pathological stage, decade of surgery and principal components as covariates. Genes in which variants were identified were studied in silico to investigate functional roles. RESULTS: Six germline variants passed the genome-wide significance threshold. These single nucleotide polymorphisms were mapped to non-coding (intronic) regions on chromosomes 2, 3, and 5. The minor alleles of rs13000315, rs151212827, and rs190923216 (chr. 2, 3 and 5, respectively) were found to be independent negative prognostic factors. All six variants have been reported to regulate the expression of nine genes, seven of which are protein-coding, in different tissues. Survival-associated variants on chromosomes 2 and 3 were already reported to regulate the expression of NT5DC2 and NAGK, with high expression associated with the minor alleles. High NT5DC2 and NAGK expression in lung adenocarcinoma tissue was already shown to correlate with poor overall survival. CONCLUSIONS: This study highlights a potential regulatory role of the identified polymorphisms in influencing outcome and suggests a mechanistic link between these variants, gene expression regulation, and lung adenocarcinoma prognosis. Validation and functional studies are warranted to elucidate the mechanisms underlying these associations.

Peripheral Inflammation Featuring Eosinophilia or Neutrophilia Is Associated with the Survival and Infiltration of Eosinophils within the Tumor among Various Histological Subgroups of Patients with NSCLC.

Immune activation status determines non-small cell lung cancer (NSCLC) prognosis, with reported positive/negative associations for T helper type 2 (TH2) responses, including allergen-specific IgE and eosinophils. Our study seeks to explore the potential impact of these comorbid immune responses on the survival rates of patients with NSCLC. Our retrospective study used data from the Data Warehouse of the German Center for Lung Research (DZL) and Lung Biobank at Thoraxklinik Heidelberg. We estimated the association of blood eosinophilia and neutrophilia on survival rates in an inflammatory cohort of 3143 patients with NSCLC. We also tested sensitization to food and inhalants and high-sensitivity C-reactive protein (hs-CRP) in a comorbidity cohort of 212 patients with NSCLC. Finally, we estimated the infiltration of immune-relevant cells including eosinophils, T-cells, and mast cells in a tissue inflammatory sub-cohort of 60 patients with NSCLC. Sensitization to at least one food or inhalant (sIgE) was higher in patients with adenocarcinoma (adeno-LC) than the non-adenocarcinoma (non-adeno-LC). Furthermore, hs-CRP was higher in non-adeno-LC compared with adeno-LC. Peripheral inflammation, particularly eosinophilia and neutrophilia, was associated with poor survival outcomes in NSCLC with a clear difference between histological subgroups. Finally, blood eosinophilia was paralleled by significant eosinophil infiltration into the peritumoral tissue in the lung. This study provides novel perspectives on the crucial role of peripheral inflammation, featuring eosinophilia and neutrophilia, with overall survival, underscoring distinctions between NSCLC subgroups (adeno-LC vs. non-adeno-LC). Peripheral eosinophilia enhances eosinophil infiltration into tumors. This sheds light on the complex interplay between inflammation, eosinophil infiltration, and NSCLC prognosis among various histological subtypes. Further studies are required to underscore the role of eosinophils in NSCLC among different histological subgroups and their role in shaping the tumor microenvironment.

ROS1 fusions in resected stage I-III adenocarcinoma: Results from the European Thoracic Oncology Platform Lungscape project.

BACKGROUND: ROS1 fusion is a relatively low prevalence (0.6-2.0%) but targetable driver in lung adenocarcinoma (LUAD). Robust and low-cost tests, such as immunohistochemistry (IHC), are desirable to screen for patients potentially harboring this fusion. The aim was to investigate the prevalence of ROS1 fusions in a clinically annotated European stage I-III LUAD cohort using IHC screening with the in vitro diagnostics (IVD)-marked clone SP384, followed by confirmatory molecular analysis in pre-defined subsets. METHODS: Resected LUADs constructed in tissue microarrays, were immunostained for ROS1 expression using SP384 clone in a ready-to-use kit and Ventana immunostainers. After external quality control, analysis was performed by trained pathologists. Staining intensity of at least 2+ (any percentage of tumor cells) was considered IHC positive (ROS1 IHC + ). Subsequently, ROS1 IHC + cases were 1:1:1 matched with IHC0 and IHC1 + cases and subjected to orthogonal ROS1 FISH and RNA-based testing. RESULTS: The prevalence of positive ROS1 expression (ROS1 IHC + ), defined as IHC 2+/3+, was 4 % (35 of 866 LUADs). Twenty-eight ROS1 IHC + cases were analyzed by FISH/RNA-based testing, with only two harboring a confirmed ROS1 gene fusion, corresponding to a lower limit for the prevalence of ROS1 gene fusion of 0.23 %. They represent a 7 % probability of identifying a fusion among ROS1 IHC + cases. Both confirmed cases were among the only four with sufficient material and H-score ≥ 200, leading to a 50 % probability of identifying a ROS1 gene fusion in cases with an H-score considered strongly positive. All matched ROS1 IHC- (IHC0 and IHC1 + ) cases were also found negative by FISH/RNA-based testing, leading to a 100 % probability of lack of ROS1 fusion for ROS1 IHC- cases. CONCLUSIONS: The prevalence of ROS1 fusion in an LUAD stage I-III European cohort was relatively low. ROS1 IHC using SP384 clone is useful for exclusion of ROS1 gene fusion negative cases.

Transbronchial Cryobiopsy Using the Ultrathin 1.1-mm Cryoprobe with Ultrathin Bronchoscopy under Radial Endobronchial Ultrasound Guidance for Diagnosis of Peripheral Pulmonary Lesions.

INTRODUCTION: Today, the increasing number of incidentally detected peripheral pulmonary lesions (PPLs) within and outside lung cancer screening trials is a diagnostic challenge. This fact encourages further improvement of diagnostic procedures to increase the diagnostic yield of transbronchial biopsy, which has been shown to have a low complication rate. The purpose of this study was to evaluate the safety and feasibility of a new ultrathin 1.1 cryoprobe that can be placed through an ultrathin bronchoscope (UTB) using fluoroscopy and radial endobronchial ultrasonography (rEBUS) navigation for assessing PPLs. METHODS: Thirty-five patients with PPL less than 4 cm in diameter were prospectively enrolled to receive transbronchial cryobiopsies (TBCBs) using the ultrathin 1.1-mm cryoprobe. Navigation to the PPL was accomplished with the UTB. Under rEBUS and fluoroscopy guidance up to 4 cryobiopsies were obtained. The sample sizes of the biopsies were compared to a historic collective derived from a 1.9-mm cryoprobe and standard forceps. The feasibility and safety of the procedure, the cumulative and overall diagnostic yield, and the cryobiopsy sizes were evaluated. RESULTS: After detection with the rEBUS, TBCB was collected from 35 PPLs, establishing a diagnosis in 25 cases, corresponding to an overall diagnostic yield of 71.4%. There was no difference in diagnostic yield for PPL <20 mm or ≥20 mm. All cryobiopsies were representative with a mean tissue area of 11.9 ± 4.3 mm2, which was significantly larger compared to the historic collective (p = 0.003). Six mild and four moderate bleeding events and 1 case of pneumothorax were observed. CONCLUSIONS: Using the ultrathin 1.1-mm cryoprobe combined with an UTB for rEBUS-guided TBCB of PPL is feasible and safe. This diagnostic approach improves bronchoscopic techniques for diagnosing peripheral lung lesions and may contribute to improve diagnosis of lung cancer even in small PPL.

PITX2 as a Sensitive and Specific Marker of Midgut Neuroendocrine Tumors: Results from a Cohort of 1157 Primary Neuroendocrine Neoplasms.

As neuroendocrine tumors (NETs) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathologic assessment. Because a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 neuroendocrine carcinomas (NEC) according to the immunoreactive score (IRS) and correlated PITX2 expression groups with general tumor groups and primary localization. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), whereas CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of neuroendocrine neoplasms. Therefore, our data argue toward implementation into diagnostic panels applied for NET as a firstline midgut marker.

CYFRA 21-1, CA 125 and CEA provide additional prognostic value in NSCLC patients with stable disease at first CT scan.

BACKGROUND: Serum tumor markers (STM) may complement imaging and provide additional clinical information for patients with non-small cell lung cancer (NSCLC). OBJECTIVE: To determine whether STMs can predict outcomes in patients with stable disease (SD) after initial treatment. METHODS: This single-center, prospective, observational trial enrolled 395 patients with stage III/IV treatment-naïve NSCLC; of which 263 patients were included in this analysis. Computed Tomography (CT) scans were performed and STMs measured before and after initial treatment (two cycles of chemotherapy and/or an immune checkpoint inhibitor or tyrosine kinase inhibitor); analyses were based on CT and STM measurements obtained at first CT performed after cycle 2 only PFS and OS were analyzed by Kaplan-Meier curves and Cox-proportional hazard models. RESULTS: When patients with SD (n = 100) were split into high- and low-risk groups based on CYFRA 21-1, CEA and CA 125 measurements using an optimized cut-off, a 4-fold increase risk of progression or death was estimated for high- vs low-risk SD patients (PFS, HR 4.17; OS, 3.99; both p < 0.0001). Outcomes were similar between patients with high-risk SD or progressive disease (n = 35) (OS, HR 1.17) and between patients with low-risk SD or partial response (n = 128) (PFS, HR 0.98; OS, 1.14). CONCLUSIONS: STMs can provide further guidance in patients with indeterminate CT responses by separating them into high- and low-risk groups for future PFS and OS events.

Prognostic value of tumor markers ProGRP, NSE and CYFRA 21-1 in patients with small cell lung cancer and chemotherapy-induced remission.

BACKGROUND: Despite successful response to first line therapy, patients with small-cell lung cancer (SCLC) often suffer from early relapses and disease progression. OBJECTIVE: To investigate the relevance of serum tumor markers for estimation of prognosis at several time points during the course of disease. METHODS: In a prospective, single-center study, serial assessments of progastrin-releasing peptide (ProGRP), neuron-specific enolase (NSE), cytokeratin-19 fragments (CYFRA 21-1) and carcino-embryogenic antigen (CEA) were performed during and after chemotherapy in 232 SCLC patients, and correlated with therapy response and overall survival (OS). RESULTS: ProGRP, NSE and CYFRA 21-1 levels decreased quickly after the first chemotherapy cycle and correlated well with the radiological response. Either as single markers or in combination they provided valuable prognostic information regarding OS at all timepoints investigated: prior to first-line therapy, after two treatment cycles in patients with successful response to first-line therapy, and prior to the start of second-line therapy. Furthermore, they were useful for continuous monitoring during and after therapy and often indicated progressive disease several months ahead of radiological changes. CONCLUSIONS: The results indicate the great potential of ProGRP, NSE and CYFRA 21-1 for estimating prognosis and monitoring of SCLC patients throughout the course of the disease.

Preliminary results from the EMoLung clinical study showing early lung cancer detection by the LC score.

BACKGROUND: Lung cancer (LC) causes more deaths worldwide than any other cancer type. Despite advances in therapeutic strategies, the fatality rate of LC cases remains high (95%) since the majority of patients are diagnosed at late stages when patient prognosis is poor. Analysis of the International Association for the Study of Lung Cancer (IASLC) database indicates that early diagnosis is significantly associated with favorable outcome. However, since symptoms of LC at early stages are unspecific and resemble those of benign pathologies, current diagnostic approaches are mostly initiated at advanced LC stages. METHODS: We developed a LC diagnosis test based on the analysis of distinct RNA isoforms expressed from the GATA6 and NKX2-1 gene loci, which are detected in exhaled breath condensates (EBCs). Levels of these transcript isoforms in EBCs were combined to calculate a diagnostic score (the LC score). In the present study, we aimed to confirm the applicability of the LC score for the diagnosis of early stage LC under clinical settings. Thus, we evaluated EBCs from patients with early stage, resectable non-small cell lung cancer (NSCLC), who were prospectively enrolled in the EMoLung study at three sites in Germany. RESULTS: LC score-based classification of EBCs confirmed its performance under clinical conditions, achieving a sensitivity of 95.7%, 91.3% and 84.6% for LC detection at stages I, II and III, respectively. CONCLUSIONS: The LC score is an accurate and non-invasive option for early LC diagnosis and a valuable complement to LC screening procedures based on computed tomography.

Krebs von den Lungen-6 as a Potential Predictive Biomarker in Fibrosing Interstitial Lung Diseases.

BACKGROUND: As fibrosing interstitial lung diseases (fILDs) are associated with high mortality, monitoring of disease activity under treatment is highly relevant. Krebs von den Lungen-6 (KL-6) is associated with the presence and severity of different fILDs, mainly in Asian patient populations. OBJECTIVES: Our aim was to evaluate KL-6 as a predictive biomarker in fILDs in Caucasian patients. METHODS: Consecutive patients with fILDs were recruited prospectively and serum concentrations of KL-6 were measured at baseline (BL), after 6 and 12 months (6 Months, 12 Months). Clinical characteristics including pulmonary function tests were assessed at 6-monthly visits and correlated with KL-6 BL levels as well as with KL-6 level changes. RESULTS: A total of 47 fILD patients were included (mean age: 65 years, 68% male). KL-6 levels at BL were significantly higher in fILD patients than in healthy controls (n = 44, mean age: 45, 23% male) (ILD: 1,757 ± 1960 U/mL vs. control: 265 ± 107 U/mL, p < 0.0001). However, no differences were noted between ILD subgroups. KL-6 decreased significantly under therapy (6M∆BL-KL6: -486 ± 1,505 mean U/mL, p = 0.032; 12M∆BL-KL6: -547 ± 1,782 mean U/mL, p = 0.041) and KL-6 level changes were negatively correlated with changes in pulmonary function parameters (forced vital capacity [FVC]: r = -0.562, p < 0.0001; DLCOSB: r = -0.405, p = 0.013). While neither absolute KL-6 levels at BL nor KL-6 level changes were associated with ILD progression (FVC decline ≥10%, DLCOSB decline ≥15% or death), patients with a stable FVC showed significantly decreasing KL-6 levels (p = 0.022). CONCLUSIONS: A decline of KL-6 under therapy correlated with a clinically relevant stabilization of lung function. Thus, KL-6 might serve as a predictive biomarker, which however must be determined by larger prospective cohorts.

Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. MATERIALS AND METHODS: In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non-neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. RESULTS: Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. CONCLUSIONS: Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer.

INTRODUCTION: The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery. MATERIALS AND METHODS: The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data. RESULTS: Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma. CONCLUSION: The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

Corrigendum: Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

[This corrects the article DOI: 10.3389/fonc.2022.1022967.].

Biological injection therapy with leukocyte-poor platelet-rich plasma induces cellular alterations, enhancement of lubricin, and inflammatory downregulation in vivo in human knees: A controlled, prospective human clinical trial based on mass spectrometry imaging analysis.

Objective: To investigate the in vivo biological effects of leukocyte-poor platelet-rich plasma (LpPRP) treatment in human synovial layer to establish the cellular basis for a prolonged clinical improvement. Methods: Synovial tissues (n = 367) were prospectively collected from patients undergoing arthroscopic surgery. Autologous-conditioned plasma, LpPRP, was injected into the knees of 163 patients 1-7 days before surgery to reduce operative trauma and inflammation, and to induce the onset of regeneration. A total of 204 patients did not receive any injection. All samples were analyzed by mass spectrometry imaging. Data analysis was evaluated by clustering, classification, and investigation of predictive peptides. Peptide identification was done by tandem mass spectrometry and database matching. Results: Data analysis revealed two major clusters belonging to LpPRP-treated (LpPRP-1) and untreated (LpPRP-0) patients. Classification analysis showed a discrimination accuracy of 82%-90%. We identified discriminating peptides for CD45 and CD29 receptors (receptor-type tyrosine-protein phosphatase C and integrin beta 1), indicating an enhancement of musculoskeletal stem cells, as well as an enhancement of lubricin, collagen alpha-1-(I) chain, and interleukin-receptor-17-E, dampening the inflammatory reaction in the LpPRP-1 group following LpPRP injection. Conclusions: We could demonstrate for the first time that injection therapy using "autologic-conditioned biologics" may lead to cellular changes in the synovial membrane that might explain the reported prolonged beneficial clinical effects. Here, we show in vivo cellular changes, possibly based on muscular skeletal stem cell alterations, in the synovial layer. The gliding capacities of joints might be improved by enhancing of lubricin, anti-inflammation by activation of interleukin-17 receptor E, and reduction of the inflammatory process by blocking interleukin-17.

Lung Adenocarcinoma Diagnosed at a Younger Age Is Associated with Advanced Stage, Female Sex, and Ever-Smoker Status, in Patients Treated with Lung Resection.

To date, the factors which affect the age at diagnosis of lung adenocarcinoma are not fully understood. In our study, we examined the relationships of age at diagnosis with smoking, pathological stage, sex, and year of diagnosis in a discovery (n = 1694) and validation (n = 1384) series of lung adenocarcinoma patients who had undergone pulmonary resection at hospitals in the Milan area and at Thoraxklinik (Heidelberg), respectively. In the discovery series, younger age at diagnosis was associated with ever-smoker status (OR = 1.5, p = 0.0035) and advanced stage (taking stage I as reference: stage III OR = 1.4, p = 0.0067; stage IV OR = 1.7, p = 0.0080), whereas older age at diagnosis was associated with male sex (OR = 0.57, p < 0.001). Analysis in the validation series confirmed the ever versus never smokers' association (OR = 2.9, p < 0.001), the association with highest stages (stage III versus stage I OR = 1.4, p = 0.0066; stage IV versus stage I OR = 2.0, p = 0.0022), and the male versus female sex association (OR = 0.78, p = 0.032). These data suggest the role of smoking in affecting the natural history of the disease. Moreover, aggressive tumours seem to have shorter latency from initiation to clinical detection. Finally, younger age at diagnosis is associated with the female sex, suggesting that hormonal status of young women confers risk to develop lung adenocarcinoma. Overall, this study provided novel findings on the mechanisms underlying age at diagnosis of lung adenocarcinoma.

Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status.

Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 (BRAF) and neuroblastoma RAS viral oncogene homolog (NRAS) are the most frequent genetic alterations in melanoma and are mutually exclusive. BRAF V600 mutations are predictive for response to the two BRAF inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to BRAF inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of BRAF and NRAS mutated and wildtype melanoma patients' tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between BRAF and NRAS mutated melanoma, and identification of both was possible with an accuracy of 87-89% and 76-79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with BRAF or NRAS mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying BRAF and NRAS mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.

[Interstitial lung diseases : Classification, differential diagnosis and treatment approaches in a heterogeneous group of chronic lung disorders]. / Interstitielle Lungenerkrankungen : Klassifikation, Differenzialdiagnostik und therapeutische Ansätze bei einer heterogenen Gruppe chronischer Lungenerkrankungen.

Interstitial lung diseases (ILD) comprise a heterogeneous group of chronic lung disorders of different etiologies that can not only affect the interstitium but also the alveolar space and the bronchial system. According to the "Global Burden of Disease Study" there has been an increase in incidence over the last decades and it is expected that the number of ILD-associated deaths will double over the next 20 years. ILD are grouped into those of unknown cause, e.g. idiopathic pulmonary fibrosis (IPF), and ILD of known cause, which include drug-induced and connective tissue disease-associated ILD as well as granulomatous ILD such as sarcoidosis and hypersensitivity pneumonitis. In addition, some ILD present a progressive fibrosing phenotype, which influences therapeutic decisions. Predominantly inflammatory entities are treated with immunosuppressives, whereas predominantly fibrosing ILD are treated with antifibrotic drugs; in some cases, a combination of both is necessary. The spectrum of differential diagnoses in ILD is broad, but definite diagnosis is essential for treatment selection; therefore, the multidisciplinary ILD board plays a pivotal role.

A Comparison of Different Sample Processing Protocols for MALDI Imaging Mass Spectrometry Analysis of Formalin-Fixed Multiple Myeloma Cells.

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. FUNDING: No specific funding was provided for this study.

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI.

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist's work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.