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Roberts EK, Luo L, Mondul AM, Banerjee M, Veenstra CM, Mariotto AB, Schipper MJ, He K, Taylor JMG, Brouwer AF. Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017. Cancer Causes Control 2024:10.1007/s10552-024-01888-y. [PMID: 38811511 DOI: 10.1007/s10552-024-01888-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/13/2024] [Indexed: 05/31/2024]
Abstract
PURPOSE Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. METHODS Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. RESULTS Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. CONCLUSION These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.
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Affiliation(s)
- Emily K Roberts
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA.
| | - Lingfeng Luo
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mousumi Banerjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Christine M Veenstra
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Angela B Mariotto
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA
| | - Matthew J Schipper
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kevin He
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeremy M G Taylor
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrew F Brouwer
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
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Ren CX, Xu GX, Dai DQ, Lin L, Sun Y, Liu QS. Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data. Med Image Anal 2024; 93:103103. [PMID: 38368752 DOI: 10.1016/j.media.2024.103103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/05/2023] [Accepted: 02/05/2024] [Indexed: 02/20/2024]
Abstract
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.
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Affiliation(s)
- Chuan-Xian Ren
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.
| | - Geng-Xin Xu
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Dao-Qing Dai
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Li Lin
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Qing-Shan Liu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Luo L, He K, Wu W, Taylor JMG. Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models. Stat Methods Med Res 2023; 32:1664-1679. [PMID: 37408385 PMCID: PMC10868332 DOI: 10.1177/09622802231181471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.
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Affiliation(s)
- Lingfeng Luo
- Department of Biostatistics, University of Michigan, Ann Arbor, US
| | - Kevin He
- Department of Biostatistics, University of Michigan, Ann Arbor, US
| | - Wenbo Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, US
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Chen D, Zha X, Ye D, Kang M, Zhu L, Yang M, Chen Y, Zhu K, Xia W, Wang Z, Wang Y. Patterns of care and prognostic evaluation for stage I-III upper esophageal squamous cell carcinoma: a population-based study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1222. [PMID: 36544690 PMCID: PMC9761128 DOI: 10.21037/atm-22-4577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/08/2022] [Indexed: 11/29/2022]
Abstract
Background There is no strong evidence regarding the optimal treatment and specific prognosis prediction model for upper esophageal squamous cell carcinoma (UESCC). This study aimed to investigate the real-world treatment patterns and develop models to predict overall survival (OS) and esophageal cancer-specific survival (ECSS) in patients with stage I-III UESCC. Methods Patients with T1-4N0-3M0 UESCC in the Surveillance, Epidemiology, and End Results (SEER) database were identified from 2010 to 2017, and randomized to a training cohort and a validation cohort. The effect of treatment patterns on survival were comprehensively analyzed. Nomograms were developed by incorporating independent prognostic factors analyzed by Cox regression in the training cohort and evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) in two cohorts. Results A total of 677 patients were identified, including 452 in the training cohort and 225 in the validation cohort. Among all populations, 71.9% (487) received chemoradiotherapy without surgery, and chemoradiotherapy or/and surgery showed better survival than other treatments. However, surgery was rarely carried out for patients with stage II-III. T stage, N stage, surgery, chemotherapy, and radiotherapy were independent risks for both OS and ECSS, while age was also an independent risk for OS. The C-indexes for nomograms to predict OS (0.71 and 0.72) and ECSS (0.70 and 0.73) were greater than 7th AJCC staging system to predict OS (0.61 and 0.64) and ECSS (0.64 and 0.64) in both the training cohort and the validation cohort. Time-dependent ROC curves and DCA also suggested that nomograms performed consistently better than 7th AJCC staging system. The calibration curves demonstrated good consistency in predicting survival. Conclusions Chemoradiotherapy was a major treatment with preferable survival for patients with stage I-III UESCC. We have firstly developed and validated prognostic nomograms in patients with stage I-III UESCC, which would play a supplementary role in the current staging system.
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Affiliation(s)
- Dongbo Chen
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaozhu Zha
- Department of Traditional Chinese Medicine, Anqing Medical College, Anqing, China
| | - Dongmei Ye
- Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mei Kang
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Liyang Zhu
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingwei Yang
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yu Chen
- Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kechao Zhu
- Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wanli Xia
- Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhi Wang
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yichun Wang
- Department of Radiation Oncology, First Affiliated Hospital of Anhui Medical University, Hefei, China
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Brouwer AF, Engle JM, Jeon J, Meza R. Sociodemographic Survival Disparities for Lung Cancer in the United States, 2000-2016. J Natl Cancer Inst 2022; 114:1492-1500. [PMID: 35866998 PMCID: PMC9664170 DOI: 10.1093/jnci/djac144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/02/2022] [Accepted: 07/19/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Understanding the impact of patient and tumor characteristics on lung cancer survival can help build personalized prognostic models and identify health disparities. METHODS We identified 557 555 patients aged 25 years and older diagnosed with lung or bronchus carcinoma from the Surveillance, Epidemiology, and End Results database, 2000-2016. We estimated hazard ratios (HR) for demographic (sex, age, race and ethnicity), tumor (stage, histology, year of diagnosis), and geographic characteristics (census tract-level urbanicity, socioeconomic status [SES]), as well as selected interactions, on the rate of lung cancer-specific death using multivariable proportional hazards models. RESULTS Women had a higher survival (lower hazard) of lung cancer-specific death than men (HR = 0.83, 95% confidence interval [CI] = 0.82 to 0.83). Hazards differed by race and ethnicity. Regional (HR = 2.41, 95% CI = 2.37 to 2.44) and distant (HR = 6.61, 95% CI = 6.53 to 6.69) tumors were associated with a lower survival (higher hazard) than localized tumors. Small cell tumors were associated with a lower survival (HR = 1.19, 95% CI = 1.18 to 1.20) than non-small cell tumors. Patients diagnosed after 2009 had lower hazards (HR = 0.86, 95% CI = 085 to 0.86) than those diagnosed 2000-2009. Lung cancer-specific survival did not depend on urbanicity after adjusting for census tract-level SES, but survival decreased with decreasing census tract-level SES. Differences in survival between non-Hispanic Black and White patients were greater for younger patients and localized tumors and increased with census tract-level SES. Differences by sex were greatest for young patients and localized tumors. CONCLUSIONS Disparities in survival after lung cancer diagnosis remain, with intersectional patterns suggesting differential access to and quality of care. Efforts are needed to ensure that high-risk groups receive guideline-concordant treatment.
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Affiliation(s)
- Andrew F Brouwer
- Correspondence to: Andrew F. Brouwer, PhD, Department of Epidemiology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA (e-mail: )
| | - Jason M Engle
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
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Faraji F, Ramirez SI, Anguiano Quiroz PY, Mendez-Molina AN, Gutkind JS. Genomic Hippo Pathway Alterations and Persistent YAP/TAZ Activation: New Hallmarks in Head and Neck Cancer. Cells 2022; 11:1370. [PMID: 35456049 PMCID: PMC9028246 DOI: 10.3390/cells11081370] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 02/06/2023] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) represents a highly prevalent and deadly malignancy worldwide. The prognosis for locoregionally advanced HNSCC has not appreciably improved over the past 30 years despite advances in surgical, radiation, and targeted therapies and less than 20% of HNSCC patients respond to recently approved immune checkpoint inhibitors. The Hippo signaling pathway, originally discovered as a mechanism regulating tissue growth and organ size, transduces intracellular and extracellular signals to regulate the transcriptional co-activators YAP and TAZ. Alterations in the Hippo pathway resulting in persistent YAP and TAZ activation have emerged as major oncogenic drivers. Our analysis of the human HNSCC oncogenome revealed multiple genomic alterations impairing Hippo signaling and activating YAP and TAZ, which in turn contribute to HNSCC development. This includes mutations and deletions of the FAT1 gene (29%) and amplification of the WWTR1 (encoding TAZ, 14%) and YAP1 genes (8%), together representing one of the most genetically altered signaling mechanisms in this malignancy. Here, we discuss key elements of the mammalian Hippo pathway, detail mechanisms by which perturbations in Hippo signaling promote HNSCC initiation and progression and outline emerging strategies to target Hippo signaling vulnerabilities as part of novel multimodal precision therapies for HNSCC.
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Affiliation(s)
- Farhoud Faraji
- Department of Otolaryngology-Head and Neck Surgery, University of California San Diego Health, La Jolla, CA 92093, USA
- Gleiberman Head and Neck Cancer Center, University of California San Diego Health, La Jolla, CA 92093, USA
- Department of Pharmacology, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA;
| | - Sydney I. Ramirez
- Department of Pharmacology, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA;
- Division of Infectious Disease and Global Public Health, Department of Internal Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | | | | | - J. Silvio Gutkind
- Gleiberman Head and Neck Cancer Center, University of California San Diego Health, La Jolla, CA 92093, USA
- Department of Pharmacology, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA;
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Wu W, Taylor JMG, Brouwer AF, Luo L, Kang J, Jiang H, He K. Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients. LIFETIME DATA ANALYSIS 2022; 28:194-218. [PMID: 35092553 PMCID: PMC9201734 DOI: 10.1007/s10985-021-09544-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.
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Affiliation(s)
- Wenbo Wu
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Jeremy M G Taylor
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Andrew F Brouwer
- Department of Epidemiology, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Lingfeng Luo
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Hui Jiang
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA
| | - Kevin He
- Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
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Puladi B, Ooms M, Kintsler S, Houschyar KS, Steib F, Modabber A, Hölzle F, Knüchel-Clarke R, Braunschweig T. Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2021; 13:4409. [PMID: 34503218 PMCID: PMC8431396 DOI: 10.3390/cancers13174409] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/24/2021] [Accepted: 08/26/2021] [Indexed: 01/01/2023] Open
Abstract
Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.
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Affiliation(s)
- Behrus Puladi
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; (B.P.); (M.O.); (A.M.); (F.H.)
- Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany; (S.K.); (F.S.); (R.K.-C.)
- Institute of Medical Informatics, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Mark Ooms
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; (B.P.); (M.O.); (A.M.); (F.H.)
| | - Svetlana Kintsler
- Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany; (S.K.); (F.S.); (R.K.-C.)
| | - Khosrow Siamak Houschyar
- Department of Dermatology and Allergology, University Hospital RWTH Aachen, 52074 Aachen, Germany;
| | - Florian Steib
- Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany; (S.K.); (F.S.); (R.K.-C.)
| | - Ali Modabber
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; (B.P.); (M.O.); (A.M.); (F.H.)
| | - Frank Hölzle
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; (B.P.); (M.O.); (A.M.); (F.H.)
| | - Ruth Knüchel-Clarke
- Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany; (S.K.); (F.S.); (R.K.-C.)
| | - Till Braunschweig
- Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany; (S.K.); (F.S.); (R.K.-C.)
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