Changes of lung parenchyma density following high dose radiation therapy for thoracic carcinomas - an automated analysis of follow up CT scans

Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics

BACKGROUND AND PURPOSE

Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation.

MATERIALS AND METHODS

Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model.

RESULTS

Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96).

CONCLUSIONS

This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.

Perioperative statin medication impairs pulmonary outcome after abdomino-thoracic esophagectomy

Background

Although surgery is the curative option of choice for patients with locally advanced esophageal cancer, morbidity, especially the rate of pulmonary complications, and consequently mortality of patients undergoing abdomino-thoracic esophagectomy remain unacceptably high. Causes for developing post-esophagectomy pulmonary complications are trauma to the lung and thoracic cavity as well as systemic inflammatory response. Statins are known to influence inflammatory pathways, but whether perioperative statin medication impacts on inflammatory response and pulmonary complication development after esophagectomy had not been investigated, yet.

Methods

Retrospective analysis and propensity score matching of patients, who either received perioperative statin medication [statin( +)] or not [statin( −)], with regard to respiratory impairment (PaO₂/FiO₂ < 300 mmHg), pneumonia development, and inflammatory serum markers after abdomino-thoracic esophagectomy.

Results

Seventy-eight patients who underwent abdomino-thoracic esophagectomy for cancer were included into propensity score pair-matched analysis [statin( +): n = 26 and statin( −): n = 52]. Although no differences were seen in postoperative inflammatory serum markers, C-reactive protein values correlated significantly with the development of pneumonia beyond postoperative day 3 in statin( −) patients. This effect was attenuated under statin medication. No difference was seen in cumulative incidences of respiratory impairment; however, significantly higher rate (65.4% versus 38.5%, p = 0.0317, OR 3.022, 95% CI 1.165–7.892) and higher cumulative incidence (p = 0.0468) of postoperative pneumonia were seen in statin( +) patients, resulting in slightly longer postoperative stay on intensive care unit (p = 0.0612) as well as significantly prolonged postoperative in-hospital stay (p = 0.0185).

Conclusions

Development of pulmonary complications after abdomino-thoracic esophagectomy is multifactorial but frequent. The establishment of preventive measures into the perioperative clinical routine is mandatory for an improved patient outcome. Perioperative medication with statins might influence pneumonia development in the highly vulnerable lung after abdomino-thoracic esophagectomy. Perioperative interruption of statin medication might be beneficial in appropriate patients; however, further clinical trials and translational studies are needed to prove this hypothesis.

The Impacts of Vertical Off-Centring, Localiser Direction, Phantom Positioning and Tube Voltage on CT Number Accuracy: An Experimental Study

Background: This study investigates the effects of vertical off-centring, localiser direction, tube voltage, and phantom positioning (supine and prone) on computed tomography (CT) numbers and radiation dose. Methods: An anthropomorphic phantom was scanned using a Discovery CT750 HD—128 slice (GE Healthcare) scanner at different tube voltages (80, 120, and 140 kVp). Images employing 0° and 180° localisers were acquired in supine and prone positions for each vertical off-centring (±100, ±60, and ±30 mm from the iso-centre). CT numbers and displayed volume CT dose index (CTDIvol) were recorded. The relationship between dose variation and CT number was investigated. Results: The maximum changes in CT number between the two phantom positions as a function of vertical-off-centring were for the upper thorax 34 HU (0° localiser, 120 kVp), mid thorax 43 HU (180° localiser, 80 kVp), and for the abdominal section 31 HU (0° localiser, 80 kVp) in the prone position. A strong positive correlation was reported between the variation in dose and CT number (r = 0.969, p < 0.001); 95% CI (0.93, 0.99). Conclusions: Patient positioning demands an approach with a high degree of accuracy, especially in cases where clinical decisions depend on CT number accuracy for tissue lesion characterisation.

Normal Lung Tissue CT Density Changes after Volumetric-Arc Radiotherapy (VMAT) for Lung Cancer

Radiation-induced lung injury remains a significant toxicity in thoracic radiotherapy. Because a precise diagnosis is difficult and commonly used assessment scales are unclear and subjective, there is a need to establish quantitative and sensitive grading methods. The lung tissue density change expressed in Hounsfield units (HUs) derived from CT scans seems a useful numeric surrogate. The study aimed to confirm a dose-response effect on HU value changes (ΔHU), their evolution in time, and the impact of selected clinical and demographic factors. We used dedicated, self-developed software to register and analyze 120 pairs of initial and follow-up CT scans of 47 lung cancer patients treated with dynamic arc radiotherapy. The differences in HU values between CT scans were calculated within discretized dose-bins limited by isodose lines. We have proved the dose-effect relationship, which is well described with a sigmoid model. We found the time evolution of HU changes to suit a typical clinical presentation of radiation-induced toxicity. Some clinical factors were found to correlate with ΔHU degree: planning target volume (PTV), V35 in the lung, patient’s age and a history of arterial hypertension, and initial lung ventilation intensity. Lung density change assessment turned out to be a sensitive and valuable method of grading post-RT lung toxicity.

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.

A 3D Agent-Based Model of Lung Fibrosis

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients

BACKGROUND

Functional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics.

MATERIALS AND METHODS

For 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated. Through deformable image registration (DIR) and quantitative image analysis, a 4DCT ventilation map was calculated. High-, medium-, and low-ventilation regions of the lung were defined based on the ventilation value. In addition, the total lung was also divided into high-, medium-, and low-density areas according to the HU threshold. The lung-avoidance plan aimed to reduce the dose to functional and high-density lungs while meeting standard target and critical structure constraints. Standard and dose-function metrics were compared between the clinical and lung-avoidance plans.

RESULTS

Lung avoidance plans led to significant reductions in high-function and high-density lung doses, without significantly increasing other organ at risk (OAR) doses, but at the expense of a significantly degraded homogeneity index (HI) and conformity index (CI; p < 0.05) of the planning target volume (PTV) and a slight increase in monitor units (MU) as well as in the number of segments (p > 0.05). Compared with the clinical plan, the mean lung dose (MLD) in the high-function and high-density areas was reduced by 0.59 Gy and 0.57 Gy, respectively.

CONCLUSION

A lung-avoidance plan based on 4DCT ventilation imaging and CT density characteristics is feasible and implementable, with potential clinical benefits. Clinical trials will be crucial to show the clinical relevance of this lung-avoidance planning strategy.

Correlation Between Lung Density Changes Under Different Dose Gradients and Radiation Pneumonitis-Based on an Analysis of Computed Tomography Scans During Esophageal Cancer Radiotherapy

Purpose

To assess the relationship between different doses of radiation and lung density changes and to determine the ability of this correlation to identify esophageal cancer (EC) patients who develop radiation pneumonitis (RP) and the occurrence time of RP.

Methods

A planning computed tomography (CT) scan and a re-planning CT scan were retrospectively collected under institutional review board approval for each of 103 thoracic segment EC patients who underwent radiotherapy (RT). The isodose curve was established on the planning CT with an interval of 5 Gy, which was used as the standard for dividing different gradient doses. Planning CT and re-planning CT scans were matched and the mean lung CT value (HU) between different doses gradients was automatically obtained by the software system. The density change value (ΔHU) was the difference of CT value between each dose gradient before and after treatment. The correlation between ΔHU and the corresponding dose was calculated, as well as the regression coefficients. Additionally the correlation between ΔHU and the occurrence and time of RP (< 4 weeks, 4-12 weeks, > 12 weeks) was calculated.

Results

The radiation dose and ΔHU was positively correlated, but the correlation coefficient and regression coefficient were lower, 0.261 (P <0.001) and 0.127 (P <0.001), respectively. With the increase of radiation dose gradient, ΔHU in RP≥2 group was higher than that in RP<2 group, and there was significant difference between two groups in ΔHU_20-25, ΔHU_25-30, ΔHU_30-35, ΔHU_35-40, ΔHU_40-45, ΔHU_45-50 (p<0.05). The occurrence time of RP was negatively correlated with the degree of ΔHU (P<0.05), with a high correlation coefficient (Y = week actual value -0.521, P < 0.001) (Y = week grade value -0.381, P = 0.004) and regression coefficient (Y = week actual value -0.503, P<0.001) (Y = week rating value -0.401, P=0.002).

Conclusions

A relationship between radiation dose and lung density changes was observed. For most dose intervals, there was an increase of ΔHU with an increased radiation dose, although low correlation coefficient. ΔHU were obvious after irradiation with dose ≥20 Gy which was closely related to the occurrence of RP. For patients with RP, the more obvious ΔHU, the earlier the occurrence of RP, there was a significant negative correlation between them.

Early Respiratory Impairment and Pneumonia after Hybrid Laparoscopically Assisted Esophagectomy-A Comparison with the Open Approach

Patients undergoing esophageal cancer surgery are at high risk of developing severe pulmonary complications. Beneficial effects of minimally invasive esophagectomy had been discussed recently, but the incidence of perioperative respiratory impairment remains unclear. This is a retrospective single-center cohort study of patients, who underwent open (OE) or laparoscopically assisted, hybrid minimally invasive abdomino-thoracic esophagectomy (LAE) for cancer regarding respiratory impairment (PaO₂/FiO₂ ratio (P/FR) < 300 mmHg) and pneumonia. No differences were observed in the cumulative incidence of reduced P/FR between OE and LAE patients. Of note, until postoperative day (POD) 2, P/FR did not differ among both groups. Thereafter, the rate of patients with respiratory impairment was higher after OE on POD 3, 5, and 10 (p ≤ 0.05) and tended being higher on POD 7 and 9 (p ≤ 0.1). Although the duration of LAE procedure was slightly longer (total: p = 0.07, thoracic part: p = 0.004), the duration of surgery (Spearman's rank correlation coefficient (r_sp) = -0.267, p = 0.006), especially of laparotomy (r_sp = -0.242, p = 0.01) correlated inversely with respiratory impairment on POD 3 after OE. Pneumonia occurred on POD 5 (1-25) and 8.5 (3-14) after OE and LAE, respectively, with the highest incidence after OE (p = 0.01). In conclusion, respiratory impairment and pulmonary complications occur frequently after esophagectomy. Although early respiratory impairment is independent of the surgical approach, postoperative pneumonia rate is reduced after LAE.

Ivor Lewis esophagectomy patients are particularly vulnerable to respiratory impairment - a comparison to major lung resection

Pulmonary complications and a poor clinical outcome are common in response to transthoracic esophagectomy, but their etiology is not well understood. Clinical observation suggests that patients undergoing pulmonary resection, a surgical intervention with similarities to the thoracic part of esophagectomy, fare much better, but this has not been investigated in detail. A retrospective single-center analysis of 181 consecutive patients after right-sided thoracotomy for either Ivor Lewis esophagectomy (n = 83) or major pulmonary resection (n = 98) was performed. An oxygenation index <300 mm Hg was used to indicate respiratory impairment. When starting surgery, respiratory impairment was seen more frequently in patients undergoing major pulmonary resection compared to esophagectomy patients (p = 0.009). On postoperative days one to ten, however, esophagectomy caused higher rates of respiratory impairment (p < 0.05) resulting in a higher cumulative incidence of postoperative respiratory impairment for patients after esophagectomy (p < 0.001). Accordingly, esophagectomy patients were characterized by longer ventilation times (p < 0.0001), intensive care unit and total postoperative hospital stays (both p < 0.0001). In conclusion, the postoperative clinical course including respiratory impairment after Ivor Lewis esophagectomy is significantly worse than that after major pulmonary resection. A detailed investigation of the underlying causes is required to improve the outcome of esophagectomy.