Investigation of the evolution of radiation-induced lung damage using serial CT imaging and pulmonary function tests

Optimizing lung cancer radiation therapy: A systematic review of multifactorial risk assessment for radiation-induced lung toxicity

BACKGROUND

Radiation therapy (RT) is essential in treating advanced lung cancer, but may lead to radiation pneumonitis (RP). This systematic review investigates the use of pulmonary function tests (PFT) and other parameters to predict and mitigate RP, thereby improving RT planning.

METHODS

A systematic review sifted through PubMed and on BioMed Central, targeting articles from September 2005 to December 2022 containing the keywords: Lung Cancer, Radiotherapy, and pulmonary function test.

RESULTS

From 1153 articles, 80 were included. RP was assessed using CTCAEv.4 in 30 % of these. Six studies evaluated post-RT quality of life in lung cancer patients, reporting no decline. Patients with RP and chronic obstructive pulmonary disease (COPD) generally exhibited poorer overall survival. Notably, forced expiratory volume in one second (FEV1) and diffusing capacity of the lung for carbon monoxide (DLCO) declined 24 months post-RT, while forced vital capacity (FVC) stayed stable. In the majority of studies, age over 60, tumors located in the lower part of the lung, and low FEV1 before RT were associated with a higher risk of RP. Dosimetric factors (V5, V20, MLD) and metabolic imaging emerged as significant predictors of RP risk. A clinical checklist blending patient and tumor characteristics, PFT results, and dosimetric criteria was proposed for assessing RP risk before RT.

CONCLUSION

The review reveals the multifactorial nature of RP development following RT in lung cancer. This approach should guide individualized management and calls for a prospective study to validate these findings and enhance RP prevention strategies.

A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans

Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.

Hemin enhances radiosensitivity of lung cancer cells through ferroptosis

The principle underlying radiotherapy is to kill cancer cells while minimizing the harmful effects on non-cancer cells, which has still remained as a major challenge. In relation, ferroptosis has recently been proposed as a novel mechanism of radiation-induced cell death. In this study, we investigated and demonstrated the role of Hemin as an iron overloading agent in the generation of reactive oxygen species (ROS) induced by ionizing radiation in lung cancer and non-cancer cells. It was found that the presence of Hemin in irradiated lung cancer cells enhanced the productivity of initial ROS, resulting in lipid peroxidation and subsequent ferroptosis. We observed that application of Hemin as a co-treatment increased the activity of GPx4 degradation in both cancer and normal lung cells. Furthermore, Hemin protected normal lung cells against radiation-induced cell death, in that it suppressed ROS after radiation, and boosted the production of bilirubin which was a lipophilic ROS antioxidant. In addition, we demonstrated significant FTH1 expression in normal lung cells when compared to lung cancer cells, which prevented iron from playing a role in increasing IR-induced cell death. Our findings demonstrated that Hemin had a dual function in enhancing the radiosensitivity of ferroptosis in lung cancer cells while promoting cell survival in normal lung cells.

A multichannel feature-based approach for longitudinal lung CT registration in the presence of radiation induced lung damage

Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung computed tomography (CT) scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness map was calculated. These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg. Traditional intensity-based registrations were also performed for comparison purposes. For the evaluation, the pre- and post-registration landmark distance was calculated for all patients, using an average of 44 manually identified landmark pairs per patient. The mean (standard deviation) distance for all datasets decreased from 15.95 (8.09) mm pre-registration to 4.56 (5.70) mm post-registration, compared to 7.90 (8.97) mm for the intensity-based registrations. Qualitative improvements in image alignment were observed for all patient datasets. For four representative subjects, registrations were performed for three additional follow-up timepoints up to 48 months post-RT and similar accuracy was achieved. We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection.

All for one, though not one for all: team players in normal tissue radiobiology

PURPOSE

As part of the special issue on 'Women in Science', this review offers a perspective on past and ongoing work in the field of normal (non-cancer) tissue radiation biology, highlighting the work of many of the leading contributors to this field of research. We discuss some of the hypotheses that have guided investigations, with a focus on some of the critical organs considered dose-limiting with respect to radiation therapy, and speculate on where the field needs to go in the future.

CONCLUSIONS

The scope of work that makes up normal tissue radiation biology has and continues to play a pivotal role in the radiation sciences, ensuring the most effective application of radiation in imaging and therapy, as well as contributing to radiation protection efforts. However, despite the proven historical value of preclinical findings, recent decades have seen clinical practice move ahead with altered fractionation scheduling based on empirical observations, with little to no (or even negative) supporting scientific data. Given our current appreciation of the complexity of normal tissue radiation responses and their temporal variability, with tissue- and/or organ-specific mechanisms that include intra-, inter- and extracellular messaging, as well as contributions from systemic compartments, such as the immune system, the need to maintain a positive therapeutic ratio has never been more urgent. Importantly, mitigation and treatment strategies, whether for the clinic, emergency use following accidental or deliberate releases, or reducing occupational risk, will likely require multi-targeted approaches that involve both local and systemic intervention. From our personal perspective as five 'Women in Science', we would like to acknowledge and applaud the role that many female scientists have played in this field. We stand on the shoulders of those who have gone before, some of whom are fellow contributors to this special issue.

In Silico Ventilation Within the Dose-Volume is Predictive of Lung Function Post-radiation Therapy in Patients with Lung Cancer

Lung cancer is a leading cause of death worldwide. Radiation therapy (RT) is one method to treat this disease. A common side effect of RT for lung cancer is radiation-induced lung damage (RILD) which leads to loss of lung function. RILD often compounds pre-existing smoking-related regional lung function impairment. It is difficult to predict patient outcomes due to large variability in individual response to RT. In this study, the capability of image-based modelling of regional ventilation in lung cancer patients to predict lung function post-RT was investigated. Twenty-five patient-based models were created using CT images to define the airway geometry, size and location of tumour, and distribution of emphysema. Simulated ventilation within the 20 Gy isodose volume showed a statistically significant negative correlation with the change in forced expiratory volume in 1 s 12-months post-RT (p = 0.001, R = - 0.61). Patients with higher simulated ventilation within the 20 Gy isodose volume had a greater loss in lung function post-RT and vice versa. This relationship was only evident with the combined impact of tumour and emphysema, with the location of the emphysema relative to the dose-volume being important. Our results suggest that model-based ventilation measures can be used in the prediction of patient lung function post-RT.

Markers Useful in Monitoring Radiation-Induced Lung Injury in Lung Cancer Patients: A Review

In 2018, lung cancer was the most common cancer and the most common cause of cancer death, accounting for a 1.76 million deaths. Radiotherapy (RT) is a widely used and effective non-surgical cancer treatment that induces remission in, and even cures, patients with lung cancer. However, RT faces some restrictions linked to the radioresistance and treatment toxicity, manifesting in radiation-induced lung injury (RILI). About 30–40% of lung cancer patients will develop RILI, which next to the local recurrence and distant metastasis is a substantial challenge to the successful management of lung cancer treatment. These data indicate an urgent need of looking for novel, precise biomarkers of individual response and risk of side effects in the course of RT. The aim of this review was to summarize both preclinical and clinical approaches in RILI monitoring that could be brought into clinical practice. Next to transforming growth factor-β1 (TGFβ1) that was reported as one of the most important growth factors expressed in the tissues after ionizing radiation (IR), there is a group of novel, potential biomarkers—microRNAs—that may be used as predictive biomarkers in therapy response and disease prognosis.