In patients with non-small-cell lung cancer (NSCLC), the use of postoperative radiotherapy (PORT) has been controversial since 1998, because of one meta-analysis showing a deleterious effect on ...survival in patients with pN0 and pN1, but with an unclear effect in patients with pN2 NSCLC. Because many changes have occurred in the management of patients with NSCLC, the role of three-dimensional (3D) conformal PORT warrants further investigation in patients with stage IIIAN2 NSCLC. The aim of this study was to establish whether PORT should be part of their standard treatment.
Lung ART is an open-label, randomised, phase 3, superiority trial comparing mediastinal PORT to no PORT in patients with NSCLC with complete resection, nodal exploration, and cytologically or histologically proven N2 involvement. Previous neoadjuvant or adjuvant chemotherapy was allowed. Patients aged 18 years or older, with an WHO performance status of 0–2, were recruited from 64 hospitals and cancer centres in five countries (France, UK, Germany, Switzerland, and Belgium). Patients were randomly assigned (1:1) to either the PORT or no PORT (control) groups via a web randomisation system, and minimisation factors were the institution, administration of chemotherapy, number of mediastinal lymph node stations involved, histology, and use of pre-treatment PET scan. Patients received PORT at a dose of 54 Gy in 27 or 30 daily fractions, on five consecutive days a week. Three dimensional conformal radiotherapy was mandatory, and intensity-modulated radiotherapy was permitted in centres with expertise. The primary endpoint was disease-free survival, analysed by intention to treat at 3 years; patients from the PORT group who did not receive radiotherapy and patients from the control group with no follow-up were excluded from the safety analyses. This trial is now closed. This trial is registered with ClinicalTrials.gov number, NCT00410683.
Between Aug 7, 2007, and July 17, 2018, 501 patients, predominantly staged with 18F-fluorodeoxyglucose (18F-FDG) PET (456 91%; 232 (92%) in the PORT group and 224 (90%) in the control group), were enrolled and randomly assigned to receive PORT (252 patients) or no PORT (249 patients). At the cutoff date of May 31, 2019, median follow-up was 4·8 years (IQR 2·9–7·0). 3-year disease-free survival was 47% (95% CI 40–54) with PORT versus 44% (37–51) without PORT, and the median disease-free survival was 30·5 months (95% CI 24–49) in the PORT group and 22·8 months (17–37) in the control group (hazard ratio 0·86; 95% CI 0·68–1·08; p=0·18). The most common grade 3–4 adverse events were pneumonitis (13 5% of 241 patients in the PORT group vs one <1% of 246 in the control group), lymphopenia (nine 4% vs 0), and fatigue (six 3% vs one <1%). Late-grade 3–4 cardiopulmonary toxicity was reported in 26 patients (11%) in the PORT group versus 12 (5%) in the control group. Two patients died from pneumonitis, partly related to radiotherapy and infection, and one patient died due to chemotherapy toxicity (sepsis) that was deemed to be treatment-related, all of whom were in the PORT group.
Lung ART evaluated 3D conformal PORT after complete resection in patients who predominantly had been staged using (18F-FDG PET-CT and received neoadjuvant or adjuvant chemotherapy. 3-year disease-free survival was higher than expected in both groups, but PORT was not associated with an increased disease-free survival compared with no PORT. Conformal PORT cannot be recommended as the standard of care in patients with stage IIIAN2 NSCLC.
French National Cancer Institute, Programme Hospitalier de Recherche Clinique from the French Health Ministry, Gustave Roussy, Cancer Research UK, Swiss State Secretary for Education, Research, and Innovation, Swiss Cancer Research Foundation, Swiss Cancer League.
Stereotactic ablative body radiotherapy (SABR) is increasingly being used to treat oligometastatic cancers, but high-level evidence to provide a basis for policy making is scarce. Additional evidence ...from a real-world setting is required. We present the results of a national study of patients with extracranial oligometastases undergoing SABR, representing the largest dataset, to our knowledge, on outcomes in this population so far.
In 2015, National Health Service (NHS) England launched a Commissioning through Evaluation scheme that funded a prospective, registry-based, single-arm, observational, evaluation study of patients with solid cancer and extracranial oligometastases treated with SABR. Prescribed doses ranged from 24–60 Gy administered in three to eight fractions. The study was done at 17 NHS radiotherapy centres in England. Patients were eligible for the scheme if aged 18 years or older with confirmed primary carcinoma (excluding haematological malignancies), one to three extracranial metastatic lesions, a disease-free interval from primary tumour development to metastases of longer than 6 months (with the exception of synchronous colorectal liver metastases), a WHO performance status of 2 or lower, and a life expectancy of at least 6 months. The primary outcome was overall survival at 1 year and 2 years from the start of SABR treatment. The study is now completed.
Between June 15, 2015, and Jan 30, 2019, 1422 patients were recruited from 17 hospitals in England. The median age of the patients was 69 years (IQR 62–76), and the most common primary tumour was prostate cancer (406 28·6% patients). Median follow-up was 13 months (IQR 6–23). Overall survival was 92·3% (95% CI 90·5–93·9) at 1 year and 79·2% (76·0–82·1) at 2 years. The most common grade 3 adverse event was fatigue (28 2·0% of 1422 patients) and the most common serious (grade 4) event was increased liver enzymes (nine 0·6%). Notreatment-related deaths were reported.
In patients with extracranial oligometastatic cancer, use of SABR was associated with high overall survival and low toxicity. ’The study findings complement existing evidence from a randomised, phase 2 trial, and represent high-level, real-world evidence supporting the use of SABR in this patient cohort, with a phase 3 randomised, controlled trial to confirm these findings underway. Based on the selection criteria in this study, SABR was commissioned by NHS England in March, 2020, as a treatment option for patients with oligometastatic disease.
NHS England Commissioning through Evaluation scheme.
Germline mutations in BRCA1/2 predispose individuals to breast cancer (termed germline-mutated BRCA1/2 breast cancer, gBRCA-BC) by impairing homologous recombination (HR) and causing genomic ...instability. HR also repairs DNA lesions caused by platinum agents and PARP inhibitors. Triple-negative breast cancers (TNBCs) harbor subpopulations with BRCA1/2 mutations, hypothesized to be especially platinum-sensitive. Cancers in putative 'BRCAness' subgroups-tumors with BRCA1 methylation; low levels of BRCA1 mRNA (BRCA1 mRNA-low); or mutational signatures for HR deficiency and those with basal phenotypes-may also be sensitive to platinum. We assessed the efficacy of carboplatin and another mechanistically distinct therapy, docetaxel, in a phase 3 trial in subjects with unselected advanced TNBC. A prespecified protocol enabled biomarker-treatment interaction analyses in gBRCA-BC and BRCAness subgroups. The primary endpoint was objective response rate (ORR). In the unselected population (376 subjects; 188 carboplatin, 188 docetaxel), carboplatin was not more active than docetaxel (ORR, 31.4% versus 34.0%, respectively; P = 0.66). In contrast, in subjects with gBRCA-BC, carboplatin had double the ORR of docetaxel (68% versus 33%, respectively; biomarker, treatment interaction P = 0.01). Such benefit was not observed for subjects with BRCA1 methylation, BRCA1 mRNA-low tumors or a high score in a Myriad HRD assay. Significant interaction between treatment and the basal-like subtype was driven by high docetaxel response in the nonbasal subgroup. We conclude that patients with advanced TNBC benefit from characterization of BRCA1/2 mutations, but not BRCA1 methylation or Myriad HRD analyses, to inform choices on platinum-based chemotherapy. Additionally, gene expression analysis of basal-like cancers may also influence treatment selection.
Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires ...specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility.
To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling.
In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC).
We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively.
The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.
Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of ...respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (
He) or xenon-129 (
Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined
He and
Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between
Xe and
He scans in the testing set. Combined training on
Xe and
He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland-Altman bias of - 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.
•Deep learning (DL) predicts overall survival of NSCLC patients receiving RT most accurately.•Dosemteric organ-at-risk variables play a key role in survival prediction.•Explainable techniques are ...utilised to provide insights into hitherto black-box DL-based survival models.
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.
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.
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.
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.
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 (
H)-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
H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory
H-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
H-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.
Older patients with early breast cancer (EBC) derive modest survival benefit from chemotherapy but have increased toxicity risk. Data on the impact of chemotherapy for EBC on quality of life in older ...patients are limited, but this is a key determinant of treatment acceptance. We aimed to investigate its effect on quality of life in older patients enrolled in the Bridging the Age Gap study.
A prospective, multicentre, observational study of EBC patients ≥70 years old was conducted in 2013–2018 at 56 UK hospitals. Demographics, patient, tumour characteristics, treatments and adverse events were recorded. Quality of life was assessed using the European Organisation for Research and Treatment of Cancer Quality-of-Life Questionnaires (EORTC-QLQ) C30, BR23 and ELD 15 plus the Euroqol-5D (eq-5d) over 24 months and analysed at each time point using baseline adjusted linear regression analysis and propensity score-matching.
Three thousand and four hundred sixteen patients were enrolled in the study; 1520 patients undergoing surgery and who had high-risk EBC were included in this analysis. 376/1520 (24.7%) received chemotherapy. At 6 months, chemotherapy had a significant negative impact in several EORTC-QLQ-C30 domains, including global health score, physical, role, social functioning, cognition, fatigue, nausea/vomiting, dyspnoea, appetite loss, diarrhoea and constipation. Similar trends were documented on other scales (EORTC-QLQ-BR23, EORTC-QLQ-ELD15 and EQ-5D-5L). Its impact was no longer significant at 18–24 months in unmatched and matched cohorts.
The negative impact of chemotherapy on quality-of-life is clinically and statistically significant at 6 months but resolves by 18 months, which is crucial to inform decision-making for older patients contemplating chemotherapy.
46099296.
•This is a multicentre, cohort study of 3416 women (aged >70 years) with breast cancer.•In older women with high-risk, early breast cancer, chemotherapy reduces quality of life.•The relevant affected domains include cognition, fatigue, physical, role and social functioning.•Chemotherapy QoL impacts are transient and largely resolve completely by 18–24 months.
Abstract Background and purpose A variety of radiotherapy fractionations are used as potentially curative treatments for non-small cell lung cancer. In the UK, 55 Gy in 20 fractions over 4 weeks ...(55/20) is the most commonly used fractionation schedule, though it has not been validated in randomized phase III trials. This audit pooled together existing data from 4 UK centres to produce the largest published series for this schedule. Materials and methods 4 UK centres contributed data (Cambridge, Cardiff, Glasgow and Sheffield). Case notes and radiotherapy records of radically treated patients between 1999 and 2007 were retrospectively reviewed. Basic patient demographics, tumour characteristics, radiotherapy and survival data were collected and analysed. Results 609 patients were identified of whom 98% received the prescribed dose of 55/20. The median age was 71.3 years, 62% were male. 90% had histologically confirmed NSCLC, 49% had stage I disease. 27% had received chemotherapy (concurrent or sequential) with their radiotherapy. The median overall survival from time of diagnosis was 24.0 months and 2 year overall survival was 50%. Conclusion These data show respectable results for patients treated with accelerated hypo-fractionated radiotherapy for NSCLC with outcomes comparable to those reported for similar schedules and represent the largest published series to date for 55/20 regime.
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