Oligometastatic non-small cell lung cancer: Impact of local and contemporary systemic treatment approaches on clinical outcome.

Oligometastatic (OMD) non-small cell lung cancer (NSCLC) is a distinct but heterogeneous entity. Current guidelines recommend systemic therapy and consolidation with local ablative therapy (LAT). However, evidence regarding the optimal choice of multimodal treatment approaches is lacking, in particular with respect to the integration of immunotherapy. This real-world study identified 218 patients with OMD NSCLC (2004-2023, prespecified criteria: ≤5 metastases in ≤2 organ systems) from three major German comprehensive cancer centers. Most patients had one (72.5%) or two (17.4%) metastatic lesions in a single (89.9%) organ system. Overall survival (OS) was significantly longer with a single metastatic lesion (HR 0.54, p = .003), and female gender (HR 0.4, p < .001). Median OS of the full cohort was 27.8 months, with 29% survival at 5 years. Patients who had completed LAT to all NSCLC sites, typically excluding patients with early progression, had a median OS of 34.4 months (37.7% 5-year OS rate) with a median recurrence-free survival (RFS) of 10.9 months (13.3% at 5 years). In those patients, systemic treatment as part of first-line therapy was associated with doubling of RFS (12.3 vs. 6.4 months, p < .001). Despite limited follow-up of patients receiving chemo-immunotherapy (EU approval 2018/2019), RFS was greatly improved by adding checkpoint inhibitors to chemotherapy (HR 0.44, p = .008, 2-year RFS 51.4% vs. 15.1%). In conclusion, patients with OMD NSCLC benefitted from multimodality approaches integrating systemic therapy and local ablation of all cancer sites. A substantial proportion of patients achieved extended OS, suggesting a potential for cure that can be further augmented with the addition of immunotherapy.

Establishing Medical Intelligence-Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study.

BACKGROUND: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. OBJECTIVE: This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making. METHODS: A Python package for the use of multimodal FHIR data (FHIRPACK [FHIR Python Analysis Conversion Kit]) was developed and pioneered in 5 real-world clinical use cases, that is, myocardial infarction, stroke, diabetes, sepsis, and prostate cancer. Patients were identified based on the ICD-10 (International Classification of Diseases, Tenth Revision) codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards. RESULTS: For 2022, a total of 1,302,988 patient encounters were analyzed. (1) Myocardial infarction: in 72.7% (261/359) of cases, medication regimens fulfilled guideline recommendations. (2) Stroke: out of 1277 patients, 165 received thrombolysis and 108 thrombectomy. (3) Diabetes: in 443,866 serum glucose and 16,180 glycated hemoglobin A1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (13,887/35,494). Among those with dysglycemia, diagnosis was coded in 44.2% (6138/13,887) of the patients. (4) Sepsis: In 1803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (773/2672, 28.9%) and piperacillin and tazobactam was the primarily prescribed antibiotic (593/1593, 37.2%). (5) PC: out of 54, three patients who received radical prostatectomy were identified as cases with prostate-specific antigen persistence or biochemical recurrence. CONCLUSIONS: Leveraging FHIR data through large-scale analytics can enhance health care quality and improve patient outcomes across 5 clinical specialties. We identified (1) patients with sepsis requiring less broad antibiotic therapy, (2) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, (3) patients who had a stroke with longer than recommended times to intervention, (4) patients with hyperglycemia who could benefit from specialist referral, and (5) patients with PC with early increases in cancer markers.

Single-Center Experience of Patients with Plasma Cell Leukemia in the Era of New Therapeutics.

INTRODUCTION: Plasma cell leukemia (PCL) can occur de novo as primary PCL (pPCL), or in patients with prior diagnosis of multiple myeloma (MM) as secondary PCL (sPCL). In 2021, the diagnostic criteria have been revised, establishing a new cut-off of ≥5% plasma cells in the peripheral blood. Lacking specific clinical trials, PCL is treated similarly to MM; however, outcome for patients with PCL remains poor. Here, we report outcomes for patients with pPCL and sPCL in the era of novel agents. METHODS: We performed a retrospective analysis and identified 19 patients (11 pPCL, 8 sPCL) who have been treated for PCL between 2010 and 2022 at University Hospital Essen. RESULTS: Patients with pPCL had a median overall survival (OS) of 37.8 months (95% CI: [15.4; 52.3] months) from diagnosis, with a median time to next treatment (TTNT) of 18.4 (2.0; 22.9) months. All patients were treated with a proteasome-inhibitor (PI)-based induction therapy, and the majority was consolidated with an autologous stem cell transplantation (SCT). Five of these patients received a tandem transplantation. Patients with sPCL had a median OS of only 1.5 months after diagnosis of PCL. Only 1 patient achieved a remission with daratumumab and subsequent allogenic SCT. CONCLUSION: With our study, we add evidence for a PI-based induction therapy followed by a consolidating autologous SCT for patients with pPCL and give further evidence that a tandem transplant concept might be beneficial. The diagnosis of sPCL remains devastating and needs new therapeutic approaches.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology.

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity.

Bispecific antibodies redirect synthetic agonistic receptor modified T cells against melanoma.

BACKGROUND: Melanoma is an immune sensitive disease, as demonstrated by the activity of immune check point blockade (ICB), but many patients will either not respond or relapse. More recently, tumor infiltrating lymphocyte (TIL) therapy has shown promising efficacy in melanoma treatment after ICB failure, indicating the potential of cellular therapies. However, TIL treatment comes with manufacturing limitations, product heterogeneity, as well as toxicity problems, due to the transfer of a large number of phenotypically diverse T cells. To overcome said limitations, we propose a controlled adoptive cell therapy approach, where T cells are armed with synthetic agonistic receptors (SAR) that are selectively activated by bispecific antibodies (BiAb) targeting SAR and melanoma-associated antigens. METHODS: Human as well as murine SAR constructs were generated and transduced into primary T cells. The approach was validated in murine, human and patient-derived cancer models expressing the melanoma-associated target antigens tyrosinase-related protein 1 (TYRP1) and melanoma-associated chondroitin sulfate proteoglycan (MCSP) (CSPG4). SAR T cells were functionally characterized by assessing their specific stimulation and proliferation, as well as their tumor-directed cytotoxicity, in vitro and in vivo. RESULTS: MCSP and TYRP1 expression was conserved in samples of patients with treated as well as untreated melanoma, supporting their use as melanoma-target antigens. The presence of target cells and anti-TYRP1 × anti-SAR or anti-MCSP × anti-SAR BiAb induced conditional antigen-dependent activation, proliferation of SAR T cells and targeted tumor cell lysis in all tested models. In vivo, antitumoral activity and long-term survival was mediated by the co-administration of SAR T cells and BiAb in a syngeneic tumor model and was further validated in several xenograft models, including a patient-derived xenograft model. CONCLUSION: The SAR T cell-BiAb approach delivers specific and conditional T cell activation as well as targeted tumor cell lysis in melanoma models. Modularity is a key feature for targeting melanoma and is fundamental towards personalized immunotherapies encompassing cancer heterogeneity. Because antigen expression may vary in primary melanoma tissues, we propose that a dual approach targeting two tumor-associated antigens, either simultaneously or sequentially, could avoid issues of antigen heterogeneity and deliver therapeutic benefit to patients.

Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer.

BACKGROUND: Personalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic and predictive markers. A strong correlation of sarcopenia or overall body composition and survival has been described. Here, we explore whether automated assessment of body composition and liver metastases from standard of care CT images can add to clinical parameters in personalized survival risk prognostication. METHODS: We retrospectively analysed clinical imaging data from 85 patients (50.6% female, mean age 58.9 SD 12.2 years) with colorectal cancer and synchronous liver metastases. Pretrained deep learning models were used to assess body composition and liver metastasis geometry from abdominal CT images before the initiation of systemic treatment. Abdominal muscle-to-bone ratio (MBR) was calculated by dividing abdominal muscle volume by abdominal bone volume. MBR was compared with body mass index (BMI), abdominal muscle volume, and abdominal muscle volume divided by height squared. Differences in overall survival based on body composition and liver metastasis parameters were compared using Kaplan-Meier survival curves. Results were correlated with clinical and biomarker data to develop a machine learning model for survival risk prognostication. RESULTS: The MBR, unlike abdominal muscle volume or BMI, was significantly associated with overall survival (HR 0.39, 95% CI: 0.19-0.80, P = 0.009). The MBR (P = 0.022), liver metastasis surface area (P = 0.01) and primary tumour sidedness (P = 0.007) were independently associated with overall survival in multivariate analysis. Body composition parameters did not correlate with KRAS mutational status or primary tumour sidedness. A prediction model based on MBR, liver metastasis surface area and primary tumour sidedness achieved a concordance index of 0.69. CONCLUSIONS: Automated segmentation enables to extract prognostic parameters from routine imaging data for personalized survival modelling in advanced colorectal cancer patients.

Deep Learning-driven classification of external DICOM studies for PACS archiving.

OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MOMO), a Deep Learning-based approach to automate this mapping process by combining metadata analysis and a neural network ensemble. METHODS: A set of 11,934 imaging series with existing anatomical labels was retrieved from the PACS database of the local hospital to train an ensemble of neural networks (DenseNet-161 and ResNet-152), which process radiological images and predict the type of study they belong to. We developed an algorithm that automatically extracts relevant metadata from imaging studies, regardless of their structure, and combines it with the neural network ensemble, forming a powerful classifier. A set of 843 anonymized external studies from 321 hospitals was hand-labeled to assess performance. We tested several variations of this algorithm. RESULTS: MOMO achieves 92.71% accuracy and 2.63% minor errors (at 99.29% predictive power) on the external study classification task, outperforming both a commercial product (82.86% accuracy, 1.36% minor errors, 96.20% predictive power) and a pure neural network ensemble (72.69% accuracy, 10.3% minor errors, 99.05% predictive power) performing the same task. We find that the highest performance is achieved by an algorithm that combines all information into one vote-based classifier. CONCLUSION: Deep Learning combined with metadata matching is a promising and flexible approach for the automated classification of external DICOM studies for PACS archiving. KEY POINTS: • The algorithm can successfully identify 76 medical study types across seven modalities (CT, X-ray angiography, radiographs, MRI, PET (+CT/MRI), ultrasound, and mammograms). • The algorithm outperforms a commercial product performing the same task by a significant margin (> 9% accuracy gain). • The performance of the algorithm increases through the application of Deep Learning techniques.