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Objective: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features., Methods: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS)., Results: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04 (pfs) /0.005 (tr) ), gradient short-run emphasis (p = 0.06 (pfs) /0.01 (tr) ), and zone percentage (p = 0.02 (pfs) /0.01 (tr) ) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01 (pfs) /0.04 (tr) ). Clustering of these features allows stratification of patients into groups of longer and shorter survival., Conclusion: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early., Critical Relevance Statement: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging., Key Points: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management., (© 2024. The Author(s).)

Introduction: Trials of CT-based screening for lung cancer have shown a mortality advantage for screening in North America and Europe. Before introducing a nationwide lung cancer screening program in Germany, it is important to assess the criteria used in international trials in the German population., Methods: We used data from 3623 lung cancer patients from the data warehouse of the German Center for Lung Research (DZL). We compared the sensitivity of the following lung cancer screening criteria overall and stratified by age and histology: the National Lung Screening Trial (NLST), the Danish Lung Cancer Screening Trial (DLCST), the 2013 and 2021 US Preventive Services Task Force (USPSTF), and an adapted version of the Prostate, Lung, Colorectal, and Ovarian no race model (adapted PLCOm2012) with 6-year risk thresholds of 1.0%/6 year and 1.7%/6 year., Results: Overall, the adapted PLCOm2012 model (1%/6 years), selected the highest proportion of lung cancer patients for screening (72.4%), followed by the 2021 USPSTF (70.0%), the adapted PLCOm2012 (1.7%/6 year) (57.4%), the 2013 USPTF (57.0%), DLCST criteria (48.7%), and the NLST (48.5%). The adapted PLCOm2012 risk model (1.0%/6 year) had the highest sensitivity for all histological types except for small-cell and large-cell carcinomas (non-significant), whereas the 2021 USPTF selected a higher proportion of patients. The sensitivity levels were higher in males than in females., Conclusion: Using a risk-based selection score resulted in higher sensitivities compared to criteria using dichotomized age and smoking history. However, gender disparities were apparent in all studied eligibility criteria. In light of increasing lung cancer incidences in women, all selection criteria should be reviewed for ways to close this gender gap, especially when implementing a large-scale lung cancer screening program., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: The advanced lung cancer inflammation index [ALI: body mass index × serum albumin/neutrophil-to-lymphocyte ratio (NLR)] reflects systemic host inflammation, and is easily reproducible. We hypothesized that ALI could assist guidance of non-small-cell lung cancer (NSCLC) treatment with immune checkpoint inhibitors (ICIs)., Patients and Methods: This retrospective study included 672 stage IV NSCLC patients treated with programmed death-ligand 1 (PD-L1) inhibitors alone or in combination with chemotherapy in 25 centers in Greece and Germany, and a control cohort of 444 stage IV NSCLC patients treated with platinum-based chemotherapy without subsequent targeted or immunotherapy drugs. The association of clinical outcomes with biomarkers was analyzed with Cox regression models, including cross-validation by calculation of the Harrell's C-index., Results: High ALI values (>18) were significantly associated with longer overall survival (OS) for patients receiving ICI monotherapy [hazard ratio (HR) = 0.402, P < 0.0001, n = 460], but not chemo-immunotherapy (HR = 0.624, P = 0.111, n = 212). Similar positive correlations for ALI were observed for objective response rate (36% versus 24%, P = 0.008) and time-on-treatment (HR = 0.52, P < 0.001), in case of ICI monotherapy only. In the control cohort of chemotherapy, the association between ALI and OS was weaker (HR = 0.694, P = 0.0002), and showed a significant interaction with the type of treatment (ICI monotherapy versus chemotherapy, P < 0.0001) upon combined analysis of the two cohorts. In multivariate analysis, ALI had a stronger predictive effect than NLR, PD-L1 tumor proportion score, lung immune prognostic index, and EPSILoN scores. Among patients with PD-L1 tumor proportion score ≥50% receiving first-line ICI monotherapy, a high ALI score >18 identified a subset with longer OS and time-on-treatment (median 35 and 16 months, respectively), similar to these under chemo-immunotherapy., Conclusions: The ALI score is a powerful prognostic and predictive biomarker for patients with advanced NSCLC treated with PD-L1 inhibitors alone, but not in combination with chemotherapy. Its association with outcomes appears to be stronger than that of other widely used parameters. For PD-L1-high patients, an ALI score >18 could assist the selection of cases that do not need addition of chemotherapy., Competing Interests: Disclosure GM reports advisory/consultation fees from Roche, AstraZeneca, Bristol Myers Squibb (BMS), Merck Sharp & Dohme (MSD), Takeda, Pfizer, Amgen, and Merck outside from the submitted work. ES reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. EZ reports advisory/consultation fees from MSD and Roche outside from the submitted work. KS reports advisory/consultation fees from MSD and Roche outside from the submitted work. SA reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Takeda, Pfizer, Amgen, and Merck outside from the submitted work. SB reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Takeda, Pfizer, and Amgen outside from the submitted work. IA reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. AC reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, and Amgen outside from the submitted work. GP reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. EL reports advisory/consultation fees from Roche, AstraZeneca, MSD, and Pfizer outside from the submitted work. HL reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. PK reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. AP reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. CA reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. EF reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. FJH reports advisory board fees and honoraria from Lilly, Roche, AstraZeneca, Novartis, Boehringer, Chiesi, Teva, Pulmonx BTG, and Olympus, as well as research funding from Lilly, Roche, AstraZeneca, Novartis, Boehringer, Chiesi, and Teva, outside of the submitted work. CE reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. GO reports advisory/consultation fees from Roche, AstraZeneca, BMS, and MSD outside from the submitted work. TM reports research funding from Roche and patents with Roche, outside from the submitted work. ZS reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, Amgen, and Merck outside from the submitted work. ER reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, and Amgen outside from the submitted work. AS reports advisory board honoraria from BMS, AstraZeneca, ThermoFisher, Novartis, speaker's honoraria from BMS, Illumina, AstraZeneca, Novartis, ThermoFisher, MSD, Roche, and research funding from Chugai, outside from the submitted work. IB reports advisory/consultation fees from Roche, AstraZeneca, BMS, MSD, Pfizer, and Amgen outside from the submitted work. MR reports personal fees from Amgen, AstraZeneca, BMS, Boehringer-Ingelheim, Lilly, Merck, MSD, Novartis, Pfizer, Roche, and Samsung, outside the submitted work. KS reports advisory/consultation fees from Roche, AstraZeneca, BMS, and MSD. MT reports advisory board honoraria from Novartis, Lilly, BMS, MSD, Roche, Celgene, Takeda, AbbVie, Boehringer, speaker's honoraria from Lilly, MSD, Takeda, research funding from AstraZeneca, BMS, Celgene, Novartis, Roche, and travel grants from BMS, MSD, Novartis, Boehringer, outside from the submitted work. PC reports research funding from AstraZeneca, Novartis, Roche, Takeda, and advisory board/lecture fees from AstraZeneca, Boehringer Ingelheim, Chugai, Novartis, Pfizer, Roche, Takeda. All other authors have declared no conflicts of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Cross-institutional biobank networks hold the promise of supporting medicine by enabling the exchange of associated samples for research purposes. Various initiatives, such as BBMRI-ERIC and German Biobank Node (GBN), aim to interconnect biobanks for enabling the compilation of joint biomaterial collections. However, building software platforms to facilitate such collaboration is challenging due to the heterogeneity of existing biobank IT infrastructures and the necessary efforts for installing and maintaining additional software components. As a remedy, this paper presents the concept of a hybrid network for interconnecting already existing software components commonly found in biobanks and a proof-of-concept implementation of two prototypes involving four biobanks of the German Biobank Node. Here we demonstrate the successful bridging of two IT systems found in many German biobanks - Samply and i2b2.

Objectives: In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically document the diagnosis and state of disease of myeloma patients. The first objective was to create a corpus consisting of free-text diagnosis paragraphs of patients with multiple myeloma from German diagnostic reports, and its manual annotation of relevant data elements by documentation specialists. The second objective was to construct and evaluate a framework using different NLP methods to enable automatic multiclass classification of relevant data elements from free-text diagnostic reports., Methods: The main diagnoses paragraph was extracted from the clinical report of one third randomly selected patients of the multiple myeloma research database from Heidelberg University Hospital (in total 737 selected patients). An EDC system was setup and two data entry specialists performed independently a manual documentation of at least nine specific data elements for multiple myeloma characterization. Both data entries were compared and assessed by a third specialist and an annotated text corpus was created. A framework was constructed, consisting of a self-developed package to split multiple diagnosis sequences into several subsequences, four different preprocessing steps to normalize the input data and two classifiers: a maximum entropy classifier (MEC) and a support vector machine (SVM). In total 15 different pipelines were examined and assessed by a ten-fold cross-validation, reiterated 100 times. For quality indication the average error rate and the average F1-score were conducted. For significance testing the approximate randomization test was used., Results: The created annotated corpus consists of 737 different diagnoses paragraphs with a total number of 865 coded diagnosis. The dataset is publicly available in the supplementary online files for training and testing of further NLP methods. Both classifiers showed low average error rates (MEC: 1.05; SVM: 0.84) and high F1-scores (MEC: 0.89; SVM: 0.92). However the results varied widely depending on the classified data element. Preprocessing methods increased this effect and had significant impact on the classification, both positive and negative. The automatic diagnosis splitter increased the average error rate significantly, even if the F1-score decreased only slightly., Conclusions: The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.