Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Chandy, E.; Szmul, Adam; Stavropoulou, Alkisti; Jacob, Joseph; Veiga, Catarina; Landau, David; Wilson, James M.; Gulliford, S.; Fenwick, John D.; Hawkins, M.; Hiley, Crispin; McClelland, Jamie R.

doi:10.3390/cancers14040946

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…proposed five different RP types, their model presents sophisticated techniques for analyzing RP. Furthermore, it was impossible to classify them intuitively from the CT images ( 26 ). Szejniuk et al.…”

Section: Discussionmentioning

confidence: 99%

A novel tool to evaluate and quantify radiation pneumonitis: A retrospective analysis of correlation of dosimetric parameters with volume of pneumonia patch

Huang

Lin²,

Chang³

et al. 2023

Front. Oncol.

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IntroductionIn lung cancer, radiation-induced lung injury (RILI) or radiation pneumonitis (RP) are major concerns after radiotherapy. We investigated the correlation between volumes of RP lesions and their RP grades after radiotherapy.Methods and materialsWe retrospectively collected data from patients with non-small lung cancer that received curative doses to the thorax without undergoing chest radiotherapy before this treatment course. The post-treatment computed tomography (CT) image was used to register to the planning CT to evaluate the correlation between dosimetric parameters and volume of pneumonia patch by using deformable image registration.ResultsFrom January 1, 2019, to December 30, 2020, 71 patients with non-small cell lung cancer with 169 sets of CT images met our criteria for evaluation. In all patient groups, we found the RPv max and RP grade max to be significant (p<0.001). Some parameters that were related to the dose-volume histogram (DVH) and RP were lung Vx (x=1-66 Gy, percentage of lung volume received ≥x Gy), and mean lung dose. Comparing these parameters of the DVH with RP grade max showed that the mean lung dose and lung V1–V31 were significantly correlated. The cut-off point for the occurrence of symptoms in all patient groups, the RPv max value, was 4.79%, while the area under the curve was 0.779. In the groups with grades 1 and 2 RP, the dose curve of 26 Gy covered ≥80% of RP lesions in >80% of patients. Patients who had radiotherapy in combination with chemotherapy had significantly shorter locoregional progression-free survival (p=0.049) than patients who received radiation therapy in combination with target therapy. Patients with RPv max >4.79% demonstrated better OS (p=0.082).ConclusionThe percentage of RP lesion volume to total lung volume is a good indicator for quantifying RP. RP lesions can be projected onto the original radiation therapy plan using coverage of the 26 Gy isodose line to determine whether the lesion is RILI.

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Section: Discussionmentioning

confidence: 99%

A novel tool to evaluate and quantify radiation pneumonitis: A retrospective analysis of correlation of dosimetric parameters with volume of pneumonia patch

Huang

Lin²,

Chang³

et al. 2023

Front. Oncol.

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“…In future work we aim to use our classes to investigate if and how tissue changes are linked to RILD pathophysiology. We have already conducted an analysis where we applied the presented classification method to investigate the degree of radiological change [ 44 ]. In that study, the longitudinal data of 24 months follow up were registered to planning scans using a dedicated multi-channel deformable registration method [ 45 ], tailored to deal with large anatomical changes.…”

Section: Discussionmentioning

confidence: 99%

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

Szmul

Chandy

Veiga

et al. 2022

Cancers

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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.

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“…The training of NTCP models relies on the classification of patients according to the development of a given toxicity. In this context, the papers by Chandy et al [ 21 ] and Szmul et al [ 22 ] present two automated classification algorithms for the analysis of RT-induced lung damage in NSCLC patients, providing novel insights into the temporal evolution of lung damage and its relationship with global and local dose as well as respiratory outcomes [ 21 ].…”

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confidence: 99%