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.

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.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer.

Background The latest large language models (LLMs) solve unseen problems via user-defined text prompts without the need for retraining, offering potentially more efficient information extraction from free-text medical records than manual annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in data mining and labeling oncologic phenotypes from free-text CT reports on lung cancer by using user-defined prompts. Materials and Methods This retrospective study included patients who underwent lung cancer follow-up CT between September 2021 and March 2023. A subset of 25 reports was reserved for prompt engineering to instruct the LLMs in extracting lesion diameters, labeling metastatic disease, and assessing oncologic progression. This output was fed into a rule-based natural language processing pipeline to match ground truth annotations from four radiologists and derive performance metrics. The oncologic reasoning of LLMs was rated on a five-point Likert scale for factual correctness and accuracy. The occurrence of confabulations was recorded. Statistical analyses included Wilcoxon signed rank and McNemar tests. Results On 424 CT reports from 424 patients (mean age, 65 years ± 11 [SD]; 265 male), GPT-4 outperformed ChatGPT in extracting lesion parameters (98.6% vs 84.0%, P < .001), resulting in 96% correctly mined reports (vs 67% for ChatGPT, P < .001). GPT-4 achieved higher accuracy in identification of metastatic disease (98.1% [95% CI: 97.7, 98.5] vs 90.3% [95% CI: 89.4, 91.0]) and higher performance in generating correct labels for oncologic progression (F1 score, 0.96 [95% CI: 0.94, 0.98] vs 0.91 [95% CI: 0.89, 0.94]) (both P < .001). In oncologic reasoning, GPT-4 had higher Likert scale scores for factual correctness (4.3 vs 3.9) and accuracy (4.4 vs 3.3), with a lower rate of confabulation (1.7% vs 13.7%) than ChatGPT (all P < .001). Conclusion When using user-defined prompts, GPT-4 outperformed ChatGPT in extracting oncologic phenotypes from free-text CT reports on lung cancer and demonstrated better oncologic reasoning with fewer confabulations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hafezi-Nejad and Trivedi in this issue.

Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension.

BACKGROUND: Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. PURPOSE: To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. STUDY TYPE: This is a prospective cohort sub-study. POPULATION: Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). ASSESSMENT: Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDPPREFUL ) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDPDCE ). Furthermore, QDPPREFUL was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. STATISTICAL TESTS: t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. RESULTS: Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDPPREFUL was significantly correlated with QDPDCE (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDPPREFUL = 33.9 ± 17.2%). DATA CONCLUSION: PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

"Will I change nodule management recommendations if I change my CAD system?"-impact of volumetric deviation between different CAD systems on lesion management.

OBJECTIVES: To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study. METHODS: In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems. RESULTS: There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems. CONCLUSIONS: Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist. KEY POINTS: • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography.

OBJECTIVES: Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition. METHODS: For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated. RESULTS: Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002). CONCLUSIONS: Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases. KEY POINTS: • Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules.

[Consensus guideline on the interdisciplinary diagnosis of interstitial lung diseases]. / S1-Leitlinie Interdisziplinäre Diagnostik interstitieller Lungenerkrankungen im Erwachsenenalter.

The evaluation of a patient with interstitial lung disease (ILD) includes assessment of clinical, radiological, and often histopathological data. As there were no specific recommendations to guide the evaluation of patients under the suspicion of an ILD within the German practice landscape, this position statement from an interdisciplinary panel of ILD experts provides guidance related to the diagnostic modalities which should be used in the evaluation of ILD. This includes clinical assessment rheumatological evaluation, radiological examinations, histopathologic sampling and the need for a final discussion in a multidisciplinary team.

Deciphering the immunosuppressive tumor microenvironment in ALK- and EGFR-positive lung adenocarcinoma.

INTRODUCTION: The advent of immune checkpoint blockade (ICB) has led to significantly improved disease outcome in lung adenocarcinoma (ADC), but response of ALK/EGFR-positive tumors to immune therapy is limited. The underlying immune biology is incompletely understood. METHODS: We performed comparative mRNA expression profiling of 31 ALK-positive, 40 EGFR-positive and 43 ALK/EGFR-negative lung ADC focused on immune gene expression. The presence and levels of tumor infiltration lymphocytes (TILs) as well as fourteen specific immune cell populations were estimated from the gene expression profiles. RESULTS: While total TILs were not lower in ALK-positive and EGFR-positive tumors compared to ALK/EGFR-negative tumors, specific immunosuppressive characteristics were detected in both subgroups: In ALK-positive tumors, regulatory T cells were significantly higher compared to EGFR-positive (fold change: FC = 1.9, p = 0.0013) and ALK/EGFR-negative tumors (FC = 2.1, p = 0.00047). In EGFR-positive tumors, cytotoxic cells were significantly lower compared to ALK-positive (FC = - 1.7, p = 0.016) and to ALK/EGFR-negative tumors (FC = - 2.1, p = 2.0E-05). A total number of 289 genes, 40 part of cytokine-cytokine receptor signaling, were differentially expressed between the three subgroups. Among the latter, five genes were differently expressed in both ALK-positive and EGFR-positive tumors, while twelve genes showed differential expression solely in ALK-positive tumors and eleven genes solely in EGFR-positive tumors. CONCLUSION: Targeted gene expression profiling is a promising tool to read out tumor microenvironment characteristics from routine diagnostic lung cancer biopsies. Significant immune reactivity including specific immunosuppressive characteristics in ALK- and EGFR-positive lung ADC, but not a total absence of immune infiltration supports further clinical evaluation of immune-modulators as partners of ICB in such tumors.

Protocol of the TREASURE study: Thoracic RadiothErapy with Atezolizumab in Small cell lUng canceR Extensive disease - a randomized, open-label, multicenter phase II trial.

BACKGROUND: Recently, the combination of the programmed death-ligand 1 (PD-L1) inhibitor atezolizumab with first-line chemotherapy has demonstrated to improve outcome for patients with advanced small cell lung cancer (SCLC), leading to approval of this regimen. At the same time, accumulating (pre-)clinical data suggest synergisms of radiotherapy and immunotherapy via the radiation-mediated induction of anti-tumor immunogenicity. Combining the recent findings, the TREASURE trial aims at further enhancing response to upfront chemo-immunotherapy by the addition of thoracic radiotherapy (TRT). METHODS/DESIGN: The TREASURE trial is a randomized, multicenter, phase II clinical trial ( ClinicalTrials.gov identifier, NCT04462276). One hundred four patients suffering from extensive disease (ED) SCLC, with any response to the standard of care induction chemo-immunotherapy will be randomized to receive atezolizumab maintenance therapy with or without TRT. The primary endpoint of this study is overall survival (OS). Secondary endpoints include further measures of efficacy, safety, and the collection of biomarker samples. A safety interim analysis will take place after n = 23 patients receiving TRT have been observed for three months after the end of TRT. DISCUSSION: This trial will investigate whether treatment efficacy can be improved by adding TRT to atezolizumab maintenance therapy in ED SCLC patients with any response after chemo-immunotherapy. Safety and feasibility of such a regimen will be evaluated, and biomaterials for a translational research project will be collected. Together, the results of this trial will deepen our comprehension of how checkpoint inhibition and radiotherapy interact and contribute to the evolving landscape of SCLC therapy. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT04462276 (Date of initial registration: 8th July 2020), https://clinicaltrials.gov/ct2/show/NCT04462276 Eudra-CT Number: 2019-003916-29 (Date of initial registration: 30th March 2020), https://www.clinicaltrialsregister.eu/ctr-search/trial/2019-003916-29/DE.

Diagnostic accuracy of automated 3D volumetry of cardiac chambers by CT pulmonary angiography for identification of pulmonary hypertension due to left heart disease.

OBJECTIVES: To assess diagnostic accuracy of automated 3D volumetry of cardiac chambers based on computed tomography pulmonary angiography (CTPA) for the differentiation of pulmonary hypertension due to left heart disease (group 2 PH) from non-group 2 PH compared to manual diameter measurements. METHODS: Patients with confirmed PH undergoing right heart catheterisation and CTPA within 100 days for diagnostic workup of PH between August 2013 and February 2016 were included in this retrospective, single-centre study. Automated 3D segmentation of left atrium, left ventricle, right atrium and right ventricle (LA/LV/RA/RV) was performed by two independent and blinded radiologists using commercial software. For comparison, axial diameters were manually measured. The ability to differentiate group 2 PH from non-group 2 PH was assessed by means of logistic regression. RESULTS: Ninety-one patients (median 67.5 years, 44 women) were included, thereof 19 patients (20.9%) classified as group 2 PH. After adjustment for age, sex and mean pulmonary arterial pressure, group 2 PH was significantly associated with larger LA volume (p < 0.001), larger LV volume (p = 0.001), lower RV/LV volume ratio (p = 0.04) and lower RV/LA volume ratio (p = 0.003). LA volume demonstrated the highest discriminatory ability to identify group 2 PH (AUC, 0.908; 95% confidence interval, 0.835-0.981) and was significantly superior to LA diameter (p = 0.009). Intraobserver and interobserver agreements were excellent for all volume measurements (intraclass correlation coefficients 0.926-0.999, all p < 0.001). CONCLUSIONS: LA volume quantified by automated, CTPA-based 3D volumetry can differentiate group 2 PH from other PH groups with good diagnostic accuracy and yields significantly higher diagnostic accuracy than left atrial diameter. KEY POINTS: • Automated cardiac chamber volumetry using non-gated CT pulmonary angiography can differentiate pulmonary hypertension due to left heart disease from other causes with good diagnostic accuracy. • Left atrial volume yields significantly higher diagnostic accuracy than left atrial axial diameter for identification of pulmonary hypertension due to left heart disease without time-consuming manual processing.

Dynamic contrast enhanced MRI of pulmonary adenocarcinomas for early risk stratification: higher contrast uptake associated with response and better prognosis.

BACKGROUND: To explore the prognostic value of serial dynamic contrast-enhanced (DCE) MRI in patients with advanced pulmonary adenocarcinoma undergoing first-line therapy with either tyrosine-kinase inhibitors (TKI) or platinum-based chemotherapy (PBC). METHODS: Patients underwent baseline (day 0, n = 98), and post-therapeutic DCE MRI (PBC: day + 1, n = 52); TKI: day + 7, n = 46) at 1.5T. Perfusion curves were acquired at 10, 40, and 70 s after contrast application and analysed semiquantitatively. Treatment response was evaluated at 6 weeks by CT (RECIST 1.1); progression-free survival (PFS) and overall survival  were analysed with respect to clinical and perfusion parameters. Relative uptake was defined as signal difference between contrast and non-contrast images, divided by the non-contrast signal. Predictors of survival were selected using Cox regression analysis. Median follow-up was 825 days. RESULTS: In pre-therapeutic and early post-therapeutic MRI, treatment responders (n = 27) showed significantly higher relative contrast uptake within the tumor at 70 s after application as compared to non-responders (n = 71, p ≤ 0.02), response defined as PR by RECIST 1.1 at 6 weeks. There was no significant change of perfusion at early MRI after treatment. In multivariate regression analysis of selected parameters, the strongest association with PFS were relative uptake at 40 s in the early post-treatment MRI and pre-treatment clinical data (presence of liver metastases, ECOG performance status). CONCLUSION: Higher contrast uptake within the tumor at pre-treatment and early post-treatment MRI was associated with treatment response and better prognosis. DCE MRI of pulmonary adenocarcinoma may provide important prognostic information.

Major clinical benefit from adjuvant chemotherapy for stage II-III non-small cell lung cancer patients aged 75 years or older: a propensity score-matched analysis.

BACKGROUND: Data are currently insufficient to support the use of adjuvant chemotherapy (ACT) after surgical resection for stage II or III non-small cell lung cancer (NSCLC) in patients aged ≥ 75 years. In this study we evaluated efficacy and safety profile of ACT in this population. METHODS: We retrospectively evaluated 140 patients ≥ 75 years who underwent curative surgical resection for stage II-III NSCLC from 2010 to 2018 with an indication to ACT according to current guidelines. A propensity score-matched analysis was performed to avoid cofounding biases. RESULTS: Thirty of 140 patients (21%) received ACT. Most patients (n = 24, 80%) received carboplatin in combination with vinorelbine, while 5 patients (17%) received cisplatin plus vinorelbine and one patient (3%) carboplatin plus gemcitabine. The occurrence of adverse events led to treatment discontinuation in 8 (27%) cases, while 19 (63%) patients completed 4 chemotherapy cycles. Common reported adverse events with ACT were anemia (n = 20, 67%), neutropenia (n = 18, 60%), thrombocytopenia (n = 9, 30%), renal impairment (n = 4, 13%) and transaminase elevation (n = 4, 13%). No toxic deaths occurred. The median follow-up was 67 months (IQR: 53-87). ACT was associated with a significant benefit in both relapse-free survival (median 36 vs. 18.5 months, p = 0.049) and overall survival (median not reached [NR] vs. 33.5 months, p = 0.023) in a propensity score-matched analysis which controlled for cofounders. CONCLUSION: ACT confers a survival benefit after curative resection of stage II-III NSCLC in selected patients aged 75 years or older with a manageable toxicity profile.

Guidance on Imaging for Invasive Pulmonary Aspergillosis and Mucormycosis: From the Imaging Working Group for the Revision and Update of the Consensus Definitions of Fungal Disease from the EORTC/MSGERC.

BACKGROUND: Clinical imaging in suspected invasive fungal disease (IFD) has a significant role in early detection of disease and helps direct further testing and treatment. Revised definitions of IFD from the EORTC/MSGERC were recently published and provide clarity on the role of imaging for the definition of IFD. Here, we provide evidence to support these revised diagnostic guidelines. METHODS: We reviewed data on imaging modalities and techniques used to characterize IFDs. RESULTS: Volumetric high-resolution computed tomography (CT) is the method of choice for lung imaging. Although no CT radiologic pattern is pathognomonic of IFD, the halo sign, in the appropriate clinical setting, is highly suggestive of invasive pulmonary aspergillosis (IPA) and associated with specific stages of the disease. The ACS is not specific for IFD and occurs in the later stages of infection. By contrast, the reversed halo sign and the hypodense sign are typical of pulmonary mucormycosis but occur less frequently. In noncancer populations, both invasive pulmonary aspergillosis and mucormycosis are associated with "atypical" nonnodular presentations, including consolidation and ground-glass opacities. CONCLUSIONS: A uniform definition of IFD could improve the quality of clinical studies and aid in differentiating IFD from other pathology in clinical practice. Radiologic assessment of the lung is an important component of the diagnostic work-up and management of IFD. Periodic review of imaging studies that characterize findings in patients with IFD will inform future diagnostic guidelines.

Overdiagnosis in lung cancer screening: Estimates from the German Lung Cancer Screening Intervention Trial.

Overdiagnosis is a major potential harm of lung cancer screening; knowing its potential magnitude helps to optimize screening eligibility criteria. The German Lung Screening Intervention Trial ("LUSI") is a randomized trial among 4052 long-term smokers (2622 men), 50.3 to 71.9 years of age from the general population around Heidelberg, Germany, comparing five annual rounds of low-dose computed tomography (n = 2029) with a control arm without intervention (n = 2023). After a median follow-up of 9.77 years postrandomization and 5.73 years since last screening, 74 participants were diagnosed with lung cancer in the control arm and 90 in the screening arm: 69 during the active screening period; of which 63 screen-detected and 6 interval cancers. The excess cumulative incidence in the screening arm (N = 16) represented 25.4% (95% confidence interval: -11.3, 64.3] of screen-detected cancer cases (N = 63). Analyzed by histologic subtype, excess incidence in the screening arm appeared largely driven by adenocarcinomas. Statistical modeling yielded an estimated mean preclinical sojourn time (MPST) of 5.38 (4.76, 5.88) years and a screen-test sensitivity of 81.6 (74.4%, 88.8%) for lung cancer overall, all histologic subtypes combined. Based on modeling, we further estimated that about 48% (47.5% [43.2%, 50.7%]) of screen-detected tumors have a lead time ≥4 years, whereas about 33% (32.8% [28.4%, 36.1%]) have a lead time ≥6 years, 23% (22.6% [18.6%, 25.7%]) ≥8 years, 16% (15.6% [12.2%, 18.3%]) ≥10 years and 11% (10.7% [8.0%, 13.0%]) ≥12 years. The high proportions of tumors with relatively long lead times suggest a major risk of overdiagnosis for individuals with comparatively short remaining life expectancies.

Joint Statement of the German Respiratory Society and German Society of Thoracic Surgery in Cooperation with the German Radiological Society: Structural Prerequisites of Centres for Interventional Treatment of Emphysema.

Interventional treatment of emphysema offers a wide range of surgical and endoscopic options for patients with advanced disease. Multidisciplinary collaboration of pulmonology, thoracic surgery, and imaging disciplines in patient selection, therapy, and follow-up ensures treatment quality. The present joint statement describes the required structural and quality prerequisites of treatment centres. This is a translation of the German article "Positionspapier der Deutschen Gesellschaft für Pneumologie und Beatmungsmedizin und der Deutschen Gesellschaft für Thoraxchirurgie in Kooperation mit der Deutschen Röntgengesellschaft: Strukturvoraussetzungen von Zentren für die interventionelle Emphysemtherapie" Pneumologie. 2020;74:17-23.

Fully Automated Segmentation of Pulmonary Fibrosis Using Different Software Tools.

OBJECTIVE: Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients. METHODS: 418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses. RESULTS: In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92). CONCLUSIONS: In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.

Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium.

BACKGROUND: Invasive fungal diseases (IFDs) remain important causes of morbidity and mortality. The consensus definitions of the Infectious Diseases Group of the European Organization for Research and Treatment of Cancer and the Mycoses Study Group have been of immense value to researchers who conduct clinical trials of antifungals, assess diagnostic tests, and undertake epidemiologic studies. However, their utility has not extended beyond patients with cancer or recipients of stem cell or solid organ transplants. With newer diagnostic techniques available, it was clear that an update of these definitions was essential. METHODS: To achieve this, 10 working groups looked closely at imaging, laboratory diagnosis, and special populations at risk of IFD. A final version of the manuscript was agreed upon after the groups' findings were presented at a scientific symposium and after a 3-month period for public comment. There were several rounds of discussion before a final version of the manuscript was approved. RESULTS: There is no change in the classifications of "proven," "probable," and "possible" IFD, although the definition of "probable" has been expanded and the scope of the category "possible" has been diminished. The category of proven IFD can apply to any patient, regardless of whether the patient is immunocompromised. The probable and possible categories are proposed for immunocompromised patients only, except for endemic mycoses. CONCLUSIONS: These updated definitions of IFDs should prove applicable in clinical, diagnostic, and epidemiologic research of a broader range of patients at high-risk.

Transbronchial cryobiopsy increases diagnostic confidence in interstitial lung disease: a prospective multicentre trial.

INTRODUCTION: The accurate diagnosis of individual interstitial lung diseases (ILD) is often challenging, but is a critical determinant of appropriate management. If a diagnosis cannot be made after multidisciplinary team discussion (MDTD), surgical lung biopsy is the current recommended tissue sampling technique according to the most recent guidelines. Transbronchial lung cryobiopsy (TBLC) has been proposed as an alternative to surgical lung biopsy. METHODS: This prospective, multicentre, international study analysed the impact of TBLC on the diagnostic assessment of 128 patients with suspected idiopathic interstitial pneumonia by a central MDTD board (two clinicians, two radiologists, two pathologists). The level of confidence for the first-choice diagnoses were evaluated in four steps, as follows: 1) clinicoradiological data alone; 2) addition of bronchoalveolar lavage (BAL) findings; 3) addition of TBLC interpretation; and 4) surgical lung biopsy findings (if available). We evaluated the contribution of TBLC to the formulation of a confident first-choice MDTD diagnosis. RESULTS: TBLC led to a significant increase in the percentage of cases with confident diagnoses or provisional diagnoses with high confidence (likelihood ≥70%) from 60.2% to 81.2%. In 32 out of 52 patients nondiagnostic after BAL, TBLC provided a diagnosis with a likelihood ≥70%. The percentage of confident diagnoses (likelihood ≥90%) increased from 22.7% after BAL to 53.9% after TBLC. Pneumothoraces occurred in 16.4% of patients, and moderate or severe bleeding in 15.7% of patients. No deaths were observed within 30 days. INTERPRETATION: TBLC increases diagnostic confidence in the majority of ILD patients with an uncertain noninvasive diagnosis, with manageable side-effects. These data support the integration of TBLC into the diagnostic algorithm for ILD.