Dataset Information

Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning.

ABSTRACT:

Purpose

Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach.

Methods

We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference.

Results

Using 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896.

Conclusions

This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.

SUBMITTER: Cheng J

PROVIDER: S-EPMC9306809 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPatients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies.Materials and methodsWe systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology.ResultsBetween 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus.ConclusionRT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making.Implications for practicePatients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.

Project description:BACKGROUND:Pneumonitis from immune checkpoint inhibitors (ICI) is a potentially fatal immune-related adverse event (irAE) from antiprogrammed death 1/programmed death ligand 1 immunotherapy. Most cases of ICI pneumonitis improve or resolve with 4-6 weeks of corticosteroid therapy. Herein, we report the incidence, clinicopathological features and management of patients with non-small cell lung cancer (NSCLC) and melanoma who developed chronic ICI pneumonitis that warrants ?12 weeks of immunosuppression. METHODS:Patients with ICI pneumonitis were identified from institutional databases of ICI-treated patients with advanced melanoma and NSCLC between January 2011 and July 2018. ICI pneumonitis was defined as clinical/radiographic evidence of lung inflammation without alternative diagnoses, adjudicated by a multidisciplinary team. Chronic ICI pneumonitis was defined as pneumonitis that persists or worsens with steroid tapering, and necessitates ?12 weeks of immunosuppression, after ICI discontinuation. Serial chest CT was used to assess radiological features, and tumor response by Response EvaluationCriteria for Solid Tumors V.1.1. Bronchoalveolar lavage fluid (BALF) samples were assessed by cell differential. Lung biopsy samples were evaluated by H&E staining and multiplex immunofluorescence (mIF), where available. RESULTS:Among 299 patients, 44 developed ICI pneumonitis (NSCLC: 5/205; melanoma: 1/94), and of these, 6 experienced chronic ICI pneumonitis. The overall incidence of chronic ICI pneumonitis was thus 2%. Of those who developed chronic ICI pneumonitis: the majority had NSCLC (5/6), all sustained disease control from ICIs, and none had other concurrent irAEs. Timing of chronic ICI pneumonitis development was variable (range: 0-50 months), and occurred at a median of 12 months post ICI start. Recrudescence of ICI pneumonitis occurred at a median of 6 weeks after initial steroid start (range: 3-12 weeks), with all patients requiring steroid reintroduction when tapered to ?10 mg prednisone/equivalent. The median total duration of steroids was 37 weeks (range: 16-43+weeks). Re-emergence of radiographic ICI pneumonitis occurred in the same locations on chest CT, in most cases (5/6). All patients who developed chronic ICI pneumonitis had BALF lymphocytosis on cell differential and organising pneumonia on lung biopsy at initial ICI pneumonitis presentation, with persistent BALF lymphocytosis and brisk CD8+ infiltration on mIF at pneumonitis re-emergence during steroid taper. CONCLUSIONS:A subset of patients who develop pneumonitis from ICIs will develop chronic ICI pneumonitis, that warrants long-term immunosuppression of ?12 weeks, and has distinct clinicopathological features.

Project description:Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Project description:PurposeWhilst survival benefits of thoracic radiotherapy (TRT) followed by immune checkpoint inhibitor (ICI) have been reported in patients with lung cancer, the potential high risk of treatment-related pneumonitis remains a concern. Asians may be more sensitive to lung toxicity than other races. This retrospective study intended to provide a comprehensive pneumonitis profile of TRT followed by ICI and investigate the risk factors from a Chinese cohort of lung cancer.Methods and materialsFrom January 2016 to July 2021, 196 patients with lung cancer who received TRT prior to ICI were retrospectively analyzed. Treatment-related pneumonitis, including checkpoint inhibitor pneumonitis (CIP), radiation pneumonitis (RP), and radiation recall pneumonitis (RRP), were recorded and graded through medical records and chest computed tomography. Characteristics predictive of pneumonitis were assessed using logistic regression models, and the receiver operating characteristic analyses were performed to identify optimal cut points for quantitative variables.ResultsWith a median follow-up of 18 months, a total of 108 patients (55.1%) developed treatment-related pneumonitis during ICI therapy, with an incidence of 25.5% for grade 2 or higher (G2+) and 4.1% for G3+. The overall rates of CIP, RP and RRP were 8.2% (n=16), 46.9% (n=92) and 7.1% (n=14), respectively. With a total mortality rate of 1.5%, vast majority of the patients recovered from pneumonitis or remained stable. No patients died of RRP. Half of the patients with G2+ RP who withheld ICI therapy restarted ICI safely after resolution of RP. The history of chronic pulmonary diseases (P=0.05), mean lung dose (MLD, P=0.038), percent volume of lung receiving ≥5 Gy (V5, P=0.012) and percent volume of lung receiving ≥20 Gy (V20, P=0.030) predicted the occurrence of RRP in univariate analyses. Interval between TRT and ICI less than 3 months was an independent predictor for G2+ treatment-related pneumonitis in a multivariate model (Odds ratio OR=2.787, P=0.004).ConclusionsTreatment-related pneumonitis, especially RRP, is acceptable and manageable in the setting of TRT followed by ICI in this Asian population. Dosimetric parameters MLD, V5 and V20 may improve the predictions of RRP in clinical practice.

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