Deep learning to estimate aging from chest imaging

Sun, 01 Jan 2023 00:00:00 +0000

Chronologic age is an important risk factor for many chronic diseases. However, as we all age at different rates chronological age is an imperfect measure of aging and more accurate estimates of one’s true biological age are desirable. The proposed study will develop and test artificial intelligence-based measures of aging from chest imaging data and will explore whether these measures can improve chronologic age-based clinical guidelines for prevention of cardiovascular disease and cancer. Over recent decades life expectancy has continuously increased, however, the years of life without significant morbidity have remained stable over the last 5 decades resulting in significant socioeconomic burden to society and the healthcare system. Therefore, delaying the onset of morbidity is a promising way to improve healthy aging. In particular, prevention and early detection of diseases of high socioeconomic importance such as cancer and cardiovascular disease are a priority to increase health and longevity. Biological age is a concept to estimate the differences in rates of aging not captured by chronologic age. Several measures of biological age have been developed in recent years including blood, functional and physiological approaches. However, there relevance in clinical routine is limited. Medical imaging may be an effective way to estimate aging by measuring age-related changes based on anatomical alterations visible in the image such as degenerative changes of the spine, dilation of the heart and vasculature and changes in lung parenchyma. Unlike molecular measures, image-based reflections of aging can be calculated opportunistically using data acquired during routine clinical care. Furthermore, these measures can estimate aging of specific organ systems, which may improve disease-specific preventive measures over general estimates of biological age. The image-based estimates of aging using deep learning proposed in this study will address this unmet need and may help to personalize clinical decision-making for chronic disease screening and prevention. My preliminary data show that deep learning can estimate a chest x-ray age from a chest radiograph image and that this chest x-ray age predicts longevity better than chronologic age in large multicenter clinical trial data. The proposed project will build on these promising initial findings with the potential to broaden the application to other imaging modalities and to leverage multi-omics data to infer causes of aging.

Collaborators:

PD. Dr. , UniKlinikum Freiburg

Outcomes

This exploratory project examined the feasibility of imaging-based phenotyping and biological aging estimation from chest CT and MRI. The work focused on conceptual development and clinical hypothesis generation rather than large-scale validation and resulted in a clinical abstract presented at the European Congress of Radiology.

Perspective

The project provided methodological insights into opportunistic imaging-based risk modeling and informed the design of subsequent projects with clearer data availability, validation strategies, and funding perspectives.

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

Sat, 01 Jan 2022 00:00:00 +0000

In Germany close to 500,000 new patients are diagnosed with cancer every year. More than half of the patients undergo radiotherapy as part of their treatment. In-room imaging, in combination with modern external beam radiotherapy techniques, enables tight adjustment of the delivered dose to the target. However, the full potential of these techniques is currently not exploited. One of the reasons for this is the presence of anatomical changes. For the vast majority of patients in today’s radiotherapy, anatomical alterations are considered by introducing safety margins around the tumor volume during treatment planning, which increases the irradiated volume, the dose burden to organs at risk (OARs), and eventually limits the applicable dose.

An improved treatment can be realized by online adaptive radiation therapy (ART): instead of applying the same irradiation plan throughout the entire course of treatment, the treatment is optimized at each irradiation session on basis of the daily anatomy in treatment position, as inferred from in-room imaging. Great efforts have been made to realize the integration of magnetic resonance imaging (MRI) as in-room imaging modality, but only during the last few years integrated MR-linacs became clinically available at few academic institutions. The superior soft-tissue contrast allows for accurate visualization of targets and OARs, which has allowed basic online ART workflows to be implemented. The time overhead associated with the generation of a synthetic-CT from MRI and a re-delineation of the patient for treatment plan optimization greatly limits applicability to all patients.

The goal of this project is to tackle the main bottlenecks of online plan adaptation: synthetic CT generation and organ delineation. By combining our respective expertise in radiotherapy and artificial intelligence, we aim at proposing deep-learning-based solutions to build a toolkit to tackle both of these challenges. In the case of synthetic CT generation, we will use generative adversarial networks with hybrid paired-unpaired training paradigms, additionally exploiting novel embedding loss functions to ensure geometric fidelity. Alternatively, disentangled representations will be investigated. For segmentation we aim at exploiting the information from previous fractions by performing spatio-temporal segmentation using longitudinal spatial memory networks. Finally, we aim at evaluating the benefits from these approaches by performing an evaluation of their clinical impact in terms of plan optimization, including physician grading of delineations and robustness assessment. The time benefits, including potential corrections to segmentations and synthetic CT, will also be quantified. Further details can be found .

Collaborators:

Prof. , Department of Radiation Oncology, LMU University Hospital
Dr. , Department of Radiation Oncology, LMU University Hospital
Prof. , CAMP, CIT School, TU Munich

Outcomes

MRI-guided online adaptive radiotherapy requires fast and reliable image segmentation and synthesis to support treatment adaptation under strict clinical time constraints. In this DFG-funded project, we addressed key methodological challenges related to patient-specific segmentation and uncertainty-aware modeling under inter-observer and inter-institutional variability. Together with the partners, we curated a longitudinal multi-center MRI cohort comprising planning and fraction scans across multiple institutions, enabling systematic evaluation under realistic clinical heterogeneity. Methodologically, the project resulted in two complementary contributions: CloverNet, a patient-specific 3D segmentation architecture leveraging prior planning annotations, and ProSona, a prompt-guided personalization framework modeling inter-expert variability via a continuous latent space. The proposed methods achieved a significant improvement over standard baselines and received a Best Paper Award at a MICCAI workshop.

Perspective

The project established a strong national research network at the interface of medical imaging, radiation oncology, and AI, and contributed to doctoral and master’s training. Ongoing work extends these approaches toward scalable foundation models for personalized and uncertainty-aware radiotherapy workflows, with a focus on clinical integration and multi-center validation.

Dfg |

Deep learning to estimate aging from chest imaging

Collaborators:

Outcomes

Perspective

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

Collaborators:

Outcomes

Perspective