Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy

BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques are largely black-box in nature, limiting explainability. We compared CPH, RSF and DL to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients receiving radiotherapy using pre-treatment covariables. We employed explainable techniques to provide insights into the contribution of each covariable on OS prediction. MATERIALS AND METHODS: The dataset contained 471 stage I-IV NSCLC patients treated with radiotherapy. We built CPH, RSF and DL OS prediction models using several baseline covariable combinations. 10-fold Monte-Carlo cross-validation was employed with a split of 70%:10%:20% for training, validation and testing, respectively. We primarily evaluated performance using the concordance index (C-index) and integrated Brier score (IBS). Local interpretable model-agnostic explanation (LIME) values, adapted for use in survival analysis, were computed for each model. RESULTS: The DL method exhibited a significantly improved C-index of 0.670 compared to the CPH and a significantly improved IBS of 0.121 compared to the CPH and RSF approaches. LIME values suggested that, for the DL method, the three most important covariables in OS prediction were stage, administration of chemotherapy and oesophageal mean radiation dose. CONCLUSION: We show that, using pre-treatment covariables, a DL approach demonstrates superior performance over CPH and RSF for OS prediction and use explainable techniques to provide transparency and interpretability

A method for quantitative analysis of regional lung ventilation using deformable image registration of CT and hybrid hyperpolarized gas/H-1 MRI

Hyperpolarized gas magnetic resonance imaging (MRI) generates highly detailed maps of lung ventilation and physiological function while CT provides corresponding anatomical and structural information. Fusion of such complementary images enables quantitative analysis of pulmonary structure-function. However, direct image registration of hyperpolarized gas MRI to CT is problematic, particularly in lungs whose boundaries are difficult to delineate due to ventilation heterogeneity. This study presents a novel indirect method of registering hyperpolarized gas MRI to CT utilizing 1H-structural MR images that are acquired in the same breath-hold as the gas MRI. The feasibility of using this technique for regional quantification of ventilation of specific pulmonary structures is demonstrated for the lobes. The direct and indirect methods of hyperpolarized gas MRI to CT image registration were compared using lung images from 15 asthma patients. Both affine and diffeomorphic image transformations were implemented. Registration accuracy was evaluated using the target registration error (TRE) of anatomical landmarks identified on 1H MRI and CT. The Wilcoxon signed-rank test was used to test statistical significance. For the affine transformation, the indirect method of image registration was significantly more accurate than the direct method (TRE = 14.7  ±  3.2 versus 19.6  ±  12.7 mm, p = 0.036). Using a deformable transformation, the indirect method was also more accurate than the direct method (TRE = 13.5  ±  3.3 versus 20.4  ±  12.8 mm, p = 0.006). Accurate image registration is critical for quantification of regional lung ventilation with hyperpolarized gas MRI within the anatomy delineated by CT. Automatic deformable image registration of hyperpolarized gas MRI to CT via same breath-hold 1H MRI is more accurate than direct registration. Potential applications include improved multi-modality image fusion, functionally weighted radiotherapy planning, and quantification of lobar ventilation in obstructive airways disease

Impact of field number and beam angle on functional image-guided lung cancer radiotherapy planning

To investigate the effect of beam angles and field number on functionally-guided intensity modulated radiotherapy (IMRT) normal lung avoidance treatment plans that incorporate hyperpolarised helium-3 magnetic resonance imaging ((3)He MRI) ventilation data. Eight non-small cell lung cancer patients had pre-treatment (3)He MRI that was registered to inspiration breath-hold radiotherapy planning computed tomography. IMRT plans that minimised the volume of total lung receiving  ⩾20 Gy (V20) were compared with plans that minimised (3)He MRI defined functional lung receiving  ⩾20 Gy (fV20). Coplanar IMRT plans using 5-field manually optimised beam angles and 9-field equidistant plans were also evaluated. For each pair of plans, the Wilcoxon signed ranks test was used to compare fV20 and the percentage of planning target volume (PTV) receiving 90% of the prescription dose (PTV90). Incorporation of (3)He MRI led to median reductions in fV20 of 1.3% (range: 0.2-9.3%; p  =  0.04) and 0.2% (range: 0 to 4.1%; p  =  0.012) for 5- and 9-field arrangements, respectively. There was no clinically significant difference in target coverage. Functionally-guided IMRT plans incorporating hyperpolarised (3)He MRI information can reduce the dose received by ventilated lung without comprising PTV coverage. The effect was greater for optimised beam angles rather than uniformly spaced fields

Comparison of 3He and129Xe MRI for evaluation of lung microstructure and ventilation at 1.5T

BACKGROUND: To support translational lung MRI research with hyperpolarized129Xe gas, comprehensive evaluation of derived quantitative lung function measures against established measures from3He MRI is required. Few comparative studies have been performed to date, only at 3T, and multisession repeatability of129Xe functional metrics have not been reported. PURPOSE/HYPOTHESIS: To compare hyperpolarized129Xe and3He MRI-derived quantitative metrics of lung ventilation and microstructure, and their repeatability, at 1.5T. STUDY TYPE: Retrospective. POPULATION: Fourteen healthy nonsmokers (HN), five exsmokers (ES), five patients with chronic obstructive pulmonary disease (COPD), and 16 patients with nonsmall-cell lung cancer (NSCLC). FIELD STRENGTH/SEQUENCE: 1.5T. NSCLC, COPD patients and selected HN subjects underwent 3D balanced steady-state free-precession lung ventilation MRI using both3He and129Xe. Selected HN, all ES, and COPD patients underwent 2D multislice spoiled gradient-echo diffusion-weighted lung MRI using both hyperpolarized gas nuclei. ASSESSMENT: Ventilated volume percentages (VV%) and mean apparent diffusion coefficients (ADC) were derived from imaging. COPD patients performed the whole MR protocol in four separate scan sessions to assess repeatability. Same-day pulmonary function tests were performed. STATISTICAL TESTS: Intermetric correlations: Spearman's coefficient. Intergroup/internuclei differences: analysis of variance / Wilcoxon's signed rank. Repeatability: coefficient of variation (CV), intraclass correlation (ICC) coefficient. RESULTS: A significant positive correlation between3He and129Xe VV% was observed (r = 0.860, P < 0.001). VV% was larger for3He than129Xe (P = 0.001); average bias, 8.79%. A strong correlation between mean3He and129Xe ADC was obtained (r = 0.922, P < 0.001). MR parameters exhibited good correlations with pulmonary function tests. In COPD patients, mean CV of3He and129Xe VV% was 4.08% and 13.01%, respectively, with ICC coefficients of 0.541 (P = 0.061) and 0.458 (P = 0.095). Mean3He and129Xe ADC values were highly repeatable (mean CV: 2.98%, 2.77%, respectively; ICC: 0.995, P < 0.001; 0.936, P < 0.001). DATA CONCLUSION:129Xe lung MRI provides near-equivalent information to3He for quantitative lung ventilation and microstructural MRI at 1.5T. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping

Implementable deep learning for multi-sequence proton MRI lung segmentation: a multi-center, multi-vendor, and multi-disease study

Background Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. Purpose Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center. Study type Retrospective. Population A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. Field Strength/Sequence 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI. Assessment 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. Statistical Tests Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. Results The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data. Data Conclusion The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. Evidence Level 4. Technical Efficacy Stage 1

Bridging The Age Gap: observational cohort study of effects of chemotherapy and trastuzumab on recurrence, survival and quality of life in older women with early breast cancer.

Background Chemotherapy improves outcomes for high risk early breast cancer (EBC) patients but is infrequently offered to older individuals. This study determined if there are fit older patients with high-risk disease who may benefit from chemotherapy. Methods A multicentre, prospective, observational study was performed to determine chemotherapy (±trastuzumab) usage and survival and quality-of-life outcomes in EBC patients aged ≥70 years. Propensity score-matching adjusted for variation in baseline age, fitness and tumour stage. Results Three thousands four hundred sixteen women were recruited from 56 UK centres between 2013 and 2018. Two thousands eight hundred eleven (82%) had surgery. 1520/2811 (54%) had high-risk EBC and 2059/2811 (73%) were fit. Chemotherapy was given to 306/1100 (27.8%) fit patients with high-risk EBC. Unmatched comparison of chemotherapy versus no chemotherapy demonstrated reduced metastatic recurrence risk in high-risk patients(hazard ratio [HR] 0.36 [95% CI 0.19–0.68]) and in 541 age, stage and fitness-matched patients(adjusted HR 0.43 [95% CI 0.20–0.92]) but no benefit to overall survival (OS) or breast cancer-specific survival (BCSS) in either group. Chemotherapy improved survival in women with oestrogen receptor (ER)-negative cancer (OS: HR 0.20 [95% CI 0.08–0.49];BCSS: HR 0.12 [95% CI 0.03–0.44]).Transient negative quality-of-life impacts were observed. Conclusions Chemotherapy was associated with reduced risk of metastatic recurrence, but survival benefits were only seen in patients with ER-negative cancer. Quality-of-life impacts were significant but transient. Trial Registration ISRCTN 4609929

Bridging the age gap: observational cohort study of effects of chemotherapy and trastuzumab on recurrence, survival and quality of life in older women with early breast cancer

Background: Chemotherapy improves outcomes for high risk early breast cancer (EBC) patients but is infrequently offered to older individuals. This study determined if there are fit older patients with high-risk disease who may benefit from chemotherapy. Methods: A multicentre, prospective, observational study was performed to determine chemotherapy (±trastuzumab) usage and survival and quality-of-life outcomes in EBC patients aged ≥70 years. Propensity score-matching adjusted for variation in baseline age, fitness and tumour stage. Results: Three thousands four hundred sixteen women were recruited from 56 UK centres between 2013 and 2018. Two thousands eight hundred eleven (82%) had surgery. 1520/2811 (54%) had high-risk EBC and 2059/2811 (73%) were fit. Chemotherapy was given to 306/1100 (27.8%) fit patients with high-risk EBC. Unmatched comparison of chemotherapy versus no chemotherapy demonstrated reduced metastatic recurrence risk in high-risk patients(hazard ratio [HR] 0.36 [95% CI 0.19–0.68]) and in 541 age, stage and fitness-matched patients(adjusted HR 0.43 [95% CI 0.20–0.92]) but no benefit to overall survival (OS) or breast cancer-specific survival (BCSS) in either group. Chemotherapy improved survival in women with oestrogen receptor (ER)-negative cancer (OS: HR 0.20 [95% CI 0.08–0.49];BCSS: HR 0.12 [95% CI 0.03–0.44]).Transient negative quality-of-life impacts were observed. Conclusions: Chemotherapy was associated with reduced risk of metastatic recurrence, but survival benefits were only seen in patients with ER-negative cancer. Quality-of-life impacts were significant but transient. Trial Registration: ISRCTN 46099296