Radiotherapy For Lung Cancer Research Articles

Patient-Reported Outcomes: Comparing Functional Avoidance and Standard Thoracic Radiation Therapy in Lung Cancer.

Novel methods generate functional images using image processing techniques combined with four-dimensional computed tomography (4DCT) data (4DCT-ventilation). 4DCT-ventilation was implemented in a phase II, multicenter functional avoidance clinical trial. The work compares functional avoidance patient-reported outcomes (PROs) against historical standards. Patients with locally advanced lung cancer undergoing curative-intent chemoradiation were accrued. 4DCT-ventilation imaging was generated and functional avoidance treatment plans created reduced dose to functional lung. PRO instruments included Functional Assessment of Cancer Therapy Lung questionnaire and accompanying subscales (including the Trial Outcome Index [TOI]), EuroQol-5 Dimension (EQ-5D), and EQ-Visual Analog Scale (EQ-VAS). The average change from baseline and percentage of clinically meaningful declines were calculated. We compared results against PROs from RTOG 0617 and PACIFIC trial data using Student t-tests and chi-square tests. Fifty-nine patients completed baseline PRO surveys. The median age was 65 (44-86) years, non-small cell lung cancer comprised 83%, and median dose was 60 Gy in 30 fractions. The percent of patients with clinically meaningful decline in FACT-TOI at 12 months was 47.8% for RTOG 0617% and 26.8% for functional avoidance (P = .03). The functional avoidance cohort demonstrated a significantly (P = .012) higher change in EQ-VAS score at 12 months (9.9 ± 3.3; average ± SE) compared with the PACIFIC cohort (1.6 ± 0.6). The current work demonstrates improved PROs from a phase II functional avoidance trial in certain subscales (FACT-TOI and EQ-VAS) compared with PROs from seminal studies (RTOG 0617 and PACIFIC). The presented data support investigation of 4DCT functional avoidance in a phase III setting.

Autoplanning in the setting of stereotactic body radiation therapy for lung cancer.

Automatic planning (AP) has been compared to manual planning (MP) in lung stereotactic body radiation therapy (SBRT) to validate the former and to implement it in clinical practice. A new developing Guided Planning System (GPS) engine was used to reoptimize 20 lung SBRT plans with the RayStation™ treatment planning system (TPS). The original manual plans were optimized to deliver 60Gy in 5 or 8 fractions to the target with constraints on organs at risk (OARs) based on an internal protocol. AP plans were compared to MP based on (i) planning target volume (PTV) and OARs dosimetric evaluation, (ii) clinician's blind plan comparison, (iii) Plan QA results, and (iv) plan quality metrics. Differences between continuous variables were explored through the Mann-Whitney test (p<0.05). Target and OARs dosimetry showed no significant difference, with the only exception of the spinal cord maximum dose that was significantly lower for AP in the 5 fractions scheme (MP: 8.93Gy±3.94Gy vs AP: 6.45Gy±2.72Gy, p=0.034). In the blind comparison, AP was preferred in 45% of cases while MP in 35% of cases (no preference was expressed in 20% of cases). A trend towards lower monitor units (MUs) was found for AP in the 5 fractions scheme (MP: 3383±943 vs AP: 2662±588, p=0.059). No significant difference was found in any of the plan quality metrics. AP plans were not inferior to MP plans; therefore, GPS is ready for clinical use in a pulmonary SBRT setting, reducing the planning workload and harmonizing the planning procedure.

Anlotinib as Maintenance Therapy After First-Line Chemotherapy Combined with Consolidation Radiation for Extensive-Stage Small Cell Lung Cancer.

Small cell lung cancer is sensitive to chemotherapy and radiotherapy, but local recurrence and distant metastasis occur shortly after treatment. This study aimed to evaluate the real-world value of anlotinib as a maintenance therapy in patients with extensive-stage small cell lung cancer (ES-SCLC) after first-line chemotherapy and consolidative thoracic radiotherapy (CTRT). A total of 150 patients with ES-SCLC treated with first-line chemotherapy and CTRT from April 2017 to December 2021 were retrospectively analyzed. After the completion of chemoradiotherapy, patients received anlotinib according to their desire. The primary endpoints were progression-free survival (PFS) and overall survival (OS) after the first diagnosis, and the secondary endpoints were prognostic factors and safety. The ORR and DCR of patients with ES-SCLC were 50.0% and 80.3%, respectively, in the anlotinib group and 42.9% and 69.0% in the no-maintenance therapy group. The 3-year OS rates were 27.6% and 12.6% in the anlotinib and observation groups (HR = 2.52, P = 0.003), and the median OS times were 23.8 months and 15.3 months. The 3-year PFS rates were 18.2% and 8.8% in the anlotinib and observation groups (HR = 1.76, P = 0.034) with median PFS times of 11.5 months and 8.8 months. After stratification on the basis of clinical response, patients who achieved CR plus PR after chemoradiotherapy had a longer median OS in the anlotinib and observation groups (34.0 months vs 24.8 months, HR = 2.40, P = 0.009). There were higher incidence rates of hand-foot syndrome (27.3% vs 10.5%, P = 0.001), gingival bleeding/hemoptysis (18.5% vs 4.8%, P = 0.015) and rash (33.3% vs 4.8%, P < 0.001) in the anlotinib group than in the observation group. Maintenance therapy with anlotinib improved the survival of patients with ES-SCLC after first-line chemotherapy and CTRT. Owing to the small sample size of the real-world trial, the reliability of our study needs to be confirmed in more studies.

Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning.

Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy (IMRT) and brachytherapy for prostate, lung, and cervical cancers. However, existing DRL planning models are built upon the Q-learning framework and rely on weighted linear combinations of clinical metrics for reward calculation. These approaches suffer from poor scalability and flexibility, that is, they are only capable of adjusting a limited number of planning objectives in discrete action spaces and therefore fail to generalize to more complex planning problems. Here we propose an automatic treatment planning model using the proximal policy optimization (PPO) algorithm in the policy gradient framework of DRL and a dose distribution-based reward function for proton PBS treatment planning of H&N cancers. The planning process is formulated as an optimization problem. A set of empirical rules is used to create auxiliary planning structures from target volumes and organs-at-risk (OARs), along with their associated planning objectives. Special attention is given to overlapping structures with potentially conflicting objectives. These planning objectives are fed into an in-house optimization engine to generate the spot monitor unit (MU) values. A decision-making policy network trained using PPO is developed to iteratively adjust the involved planning objective parameters. The policy network predicts actions in a continuous action space and guides the treatment planning system to refine the PBS treatment plans using a novel dose distribution-based reward function. A total of 34 H&N patients (30 for training and 4 for test) and 26 liver patients (20 for training, 6 for test) are included in this study to train and verify the effectiveness and generalizability of the proposed method. Proton H&N treatment plans generated by the model show improved OAR sparing with equal or superior target coverage when compared with human-generated plans. Moreover, additional experiments on liver cancer demonstrate that the proposed method can be successfully generalized to other treatmentsites. The automatic treatment planning model can generate complex H&N plans with quality comparable or superior to those produced by experienced human planners. Compared with existing works, our method is capable of handling more planning objectives in continuous action spaces. To the best of our knowledge, this is the first DRL-based automatic treatment planning model capable of achieving human-level performance for H&Ncancers.

Intricacies of human–AI interaction in dynamic decision-making for precision oncology

AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals’ cancer progression for effective personalized care. However, AI’s imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human–AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma. We investigated two levels of collaborative behavior: model-agnostic and model-specific; and found that Human–AI interaction is multifactorial and depends on the complex interrelationship between prior knowledge and preferences, patient’s state, disease site, treatment modality, model transparency, and AI’s learned behavior and biases. In summary, some clinicians may disregard AI recommendations due to skepticism; others will critically analyze AI recommendations on a case-by-case basis; clinicians will adjust their decisions if they find AI recommendations beneficial to patients; and clinician will disregard AI recommendations if deemed harmful or suboptimal and seek alternatives.

Radiotherapy(R) Integration(I) Strategy for Small(S)-Cell Lung Cancer in Extensive(E) Stage (RISE) with up to 10 metastases- a study protocol of a randomized phase II trial

Background The current standard of care (SoC) for patients with extensive-disease small-cell lung cancer (ED-SCLC) is chemo-immunotherapy. The efficacy of radiotherapy (RT) for chest consolidation has been established for patients with ED-SCLC who have responded to chemotherapy. There is a lack of data on incorporating RT as chest consolidation and metastasis-directed therapy for ED-SCLC. The RISE (Radiotherapy for Extensive-Stage Small-Cell Lung Cancer) study aims to evaluate the effectiveness of different RT strategies for residual lesions for patients with ED-SCLC who receive chemo-immunotherapy.MethodsA total of 165 patients with ED-SCLC will be recruited, with 55 patients assigned to each of the three study arms. Patients with stabilization or partial regression, according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, during chemo-immunotherapy will be included.• Arm I will serve as the control group, comprising patients who continue SoC of programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) immunotherapy (durvalumab or atezolizumab) following platinum-based chemo-immunotherapy.• Arm II will receive the SoC with consolidative RT to the chest area and potentially, according to palliative indications to metastatic lesions, delivered in 30 Gy in 3-Gy fractions.• Arm III will receive SoC with RT of 45 Gy in 3-Gy fractions to the chest area and stereotactic body radiotherapy (SBRT) with 24 Gy in 8-Gy fractions to the metastatic lesions.Blood samples for circulating tumor DNA (ctDNA) will be collected before RT, during each week of treatment, and at the time of disease progression.The primary endpoint is progression-free survival (PFS) based on RECIST 1.1 or patient death. 1. Secondary endpoints are OS, treatment toxicity (frequency of G3 toxicity according to CTCAE v.5.0), area of progression (primary tumor localization/new lesions), Overall response rate (ORR), and the response rate in non-irradiated lesions.DiscussionThe study population of patients with ED-SCLC has a poor prognosis. Dose-escalated chest RT and SBRT (for up to 10 metastases) administered with modern techniques offer the possibility to improve OS and PFS.Trial registrationClinicaltrials.gov NCT06529081 (Registered 26th Jul 2024).

The role of radiotherapy in small cell lung cancer: a new paradigm for the radiation oncologist

Small cell lung cancer (SCLC) is an aggressive tumor that presents in most cases as a metastatic disease. The prognosis is poor, but the advent of immunotherapy has rekindled hopes for outcomes. Radiotherapy plays a crucial role in this oncological scenario, and there are still many open questions on the correct application of radiotherapy and its integration with chemotherapy and immunotherapy. These issues are of great interest to the oncology community; among these, in particular, there are the choice of optimal fractionation and total dose for thoracic radiotherapy in limited SCLC and its biological implications, the role of prophylactic cranial irradiation and thoracic consolidation in the context of modern treatments with chemoimmunotherapy in extensive SCLC, the role and indications of stereotactic radiotherapy in oligometastatic scenario and finally the complex clinical and multidisciplinary management of SCLC. This perspective article aims to describe the strengths and limitations of the role of radiotherapy in SCLC, highlighting the critical role of radiotherapy and the radiation oncologist, with the need to implement specific knowledge and skills on SCLC.

Prognostic analysis of helical tomotherapy stereotactic body radiotherapy in multiple primary or second primary lung cancers

PurposeTo evaluate the safety and efficacy of Helical Tomotherapy stereotactic body radiotherapy (HT-SBRT) in treating multiple primary lung cancers (MPLCs) and second primary lung cancer (SPLC).MethodsFrom January 2010 to September 2023, 106 MPLCs and SPLC (T1-3N0M0) underwent HT-SBRT. The cumulative incidence for local recurrence (LR) was calculated using the competing risk approach and compared using Gray’s test. Cancer-specific survival (CSS) and progression-free survival (PFS) were assessed using Kaplan-Meier analysis and log-rank tests.ResultsAfter adjusting for competing risks, the LR rates for all lesions (n = 150) was 15.3%, with 2- and 4-year rates of 7.5% and 11.4%. For second primary lung nodules post-surgery, the cumulative incidence of LR was 16.1%, with 2- and 4-year rates of 6.9% and 8.7%. In MPLCs treated with HT-SBRT, the cumulative incidence of LR was 14.3%, with 2- and 4-year rates of 8.2% and 14.3%. In patients with MPLCs treated with HT-SBRT (n = 27), the CSS rates at 2, 4, and 10 years were 90.5%, 78.6%, and 53.6%, respectively, and the PFS rates were 59.5%, 32.8%, and 24.6%. In patients with SPLC who received HT-SBRT after surgery (n = 79), the CSS rates at 2, 4, and 10 years were 90.9%, 81.7%, and 61.0%, respectively, while the PFS rates were 75.4%, 64.4%, and 58.5%. Additionally, 0.9% of patients experienced grade 3 acute radiation pneumonitis, and no severe (grade 4–5) toxicities were reported.ConclusionsHT-SBRT may be a safe and effective treatment for MPLCs and SPLC, though prospective studies are needed to confirm its efficacy.

Advances in adjuvant therapy for operable N2 non-small cell lung cancer: a narrative review.

Non-small cell lung cancer (NSCLC) is still the disease with the highest incidence rate among malignant tumors, in which NSCLC under N2 stage has obvious survival differences among different patients due to its high heterogeneity. For NSCLC under this stage, the current treatment options are: preoperative neoadjuvant therapy, surgical treatment, postoperative adjuvant chemotherapy, postoperative adjuvant radiotherapy (PORT), Postoperative adjuvant targeted therapy and postoperative adjuvant immunotherapy. Whether postoperative adjuvant radiotherapy is routinely administered to patients with pN2 remains controversial in clinical application. Meanwhile, the booming development of adjuvant targeted therapy and adjuvant immunotherapy also provides newer therapeutic options for the prognosis of postoperative pN2 stage NSCLC, and some new markers will guide the adaptive application of immune drugs in the future. This article analyzes the current stage of therapeutic advances in operable stage N2 non-small cell lung cancer, and discusses in detail in this article the therapeutic controversy of postoperative adjuvant radiotherapy in pN2 stage non-small cell lung cancer, so as to explore a more reasonable treatment mode for future patients with stage N2 non-small cell lung cancer.

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy

Imaging Manifestations Following Radiation Therapy for Lung Cancer

Imaging Manifestations Following Radiation Therapy for Lung Cancer

Adaptive treatment margins to reduce organs at risk dose in patients with no or minimal anatomical changes in radiotherapy of non-small cell lung cancer

Adaptive treatment margins to reduce organs at risk dose in patients with no or minimal anatomical changes in radiotherapy of non-small cell lung cancer

Comparing the Robustness of Intensity-modulated Proton Therapy and Proton-arc Therapy Against Interplay Effects of 4D Robust-optimised Plans for Lung Stereotactic Body Radiotherapy.

To assess the robustness of 4D-optimised IMPT and PAT plans against interplay effects in non-small cell lung cancer (NSCLC) patients with respiratory motion over 10 mm, and to provide insights into the use of proton-based stereotactic body radiotherapy (SBRT) for lung cancer with significant tumour movement. Fourteen patients with early-stage NSCLC and tumour motion >10 mm were selected. Three hypofraction regimens were generated using 4D robust optimisation with the IMPT and PAT techniques. The nominal plan qualities for both techniques were compared, and their robustness against setup and range uncertainties was evaluated. 4D dynamic dose and the 4D static dose were generated to calculate ΔIMR(%) for interplay effects. PAT plans demonstrated superior target metrics such as D95 and D2, and offered enhanced protection for organs at risk (OARs), particularly in lung metrics, across multiple fractionation schemes (p < 0.05). The robustness of target coverage against setup and range uncertainties was better in PAT plans than IMPT, with average pass rates of 97.8% and 95.4%, respectively (p < 0.01). The interplay effect significantly affected target metrics in single-fraction plans, decreasing with more fractions, while its effect on OAR metrics was minimal. Median values for single-fraction plans were: ΔID98GTV was -3% for IMPT and -0.7% for PAT (p < 0.01); ΔID95GTV was -2.4% for IMPT and -0.6% for PAT (p < 0.01); ΔID2GTV was 3.2% for IMPT and 0.9% for PAT (p < 0.05). The interplay effects resulted in median homogeneity index deviations of 9.1% and 2% for the IMPT and PAT plans, respectively (p < 0.01). Different starting phases affected IMPT more significantly than PAT. PAT demonstrated greater robustness to interplay effects than IMPT for hypofractionated treatments of early-stage NSCLC, particularly in single-fraction schemes. Additionally, PAT showed good resilience to variations in different starting phases.

Applying aperture-based intensity map in automated plan review of volumetric modulated arc therapy for lung cancer patients.

Volumetric modulated arc therapy (VMAT) is a popular radiotherapy technique in the clinic. As consisting of hundreds of control points in a VMAT plan it is more complex and time consuming than those conventional treatment modalities, such as intensity modulated radiation therapy. To improve the efficiency and accuracy of its quality assurance procedure, a novel automated anomaly detection method was proposed. The anomaly detection model was the Vanilla AutoEncoder (AE). The input was the aperture-based feature maps extracted from the VMAT treatment plan. The output was the reconstruction error in measuring the original and reconstructed feature maps via the low-dimensional latent variables in the bottleneck of the AE model. The AE model was first trained with feature maps extracted from regular plans, and a detection threshold alpha (α) over the distribution of reconstruction errors was then determined. If a larger reconstruction error was obtained for the testing plan, it was considered an anomaly. The data of VMAT plans of 677 patients undergoing lung cancer radiotherapy were collected and tested. The proposed AE was compared with four other classic detection models (principal components analysis, isolation forest, local outlier factor, and hierarchical density-based spatial clustering of applications with noise) using the testing set. To evaluate its reliability, two types of perturbation factors [leaf offset and monitor unit (MU)] were assessed. Among the five tested models, the AE model achieved the best performance with the area under the receiver operating characteristic curve equal to 0.943. The accuracy and precision of the AE model were 0.769 and 0.407, respectively, which were the highest among the five models. Additionally, in terms of reliability, the AE model was more sensitive in detecting leaf offset and less sensitive in detecting MU variation. In the automatic physics review of radiotherapy plans, the application of two-dimensional aperture-based feature maps to detect irregular VMAT plans via the AE model is both viable and effective for lung cancer patients.

Dosimetric Planning Comparison for Left Ventricle Avoidance in Non-small Cell Lung Cancer Radiotherapy.

Radiation may unintentionally injure myocardial tissue, potentially leading to radiation-induced cardiac disease (RICD), with the net benefit of non-small cell lung cancer (NSCLC) radiotherapy (RT) due to the proximity of the lung and heart. RTOG-0617 showed a greater reduction in overall survival (OS) comparing higher doses to standard radiation doses in NSCLC RT. V5GyHeart has been reported as an OS predictor in the first- and fifth-year follow-ups. A worsening OS trend was reported in another study where the mean left ventricle dose (mean LV) was ≥14.5 Gy. It is therefore important to spare the heart, specifically the LV, from radiation. Furthermore, dose-limiting factors toward the normal lung should be accounted for to prevent radiation-induced lung injury. The LV and left anterior descending artery (LAD) were also contoured on the average four-dimensional computed tomography (4D-CT) dataset that contained clinically defined targets and normal structures for stage III NSCLC RT. The prescribed treatment plans (n=15) were retrospectively optimized with the clinical goals of minimizing the mean LV and mean heart dose while maintaining the dose constraint of V20GyLung ≤30% and V95%PTV ≥95%. Dose-volume histograms were used to compare the heart and lung dosimetric parameters between the delivered and reoptimized RT plans. A significant reduction (p≤0.044) was observed in the mean LV, mean heart dose, mean LAD dose, max LADdose, and V5GyHeart from the reoptimized RT plans. V20GyLung ≤30% and V95%PTV ≥95% were maintained, and no differences were observed in the mean lung, V5GyLung, V20GyLung, mean esophagus, and max cord. Minimizing the LV dose in NSCLC RT plans is achievable and dosimetrically advantageous for the heart while maintaining dose constraints to the normal lung and maximizing tumor control. Radiation dose reduction to cardiac substructures may decrease the RICD risk in NSCLC patients.

Outcomes of patients receiving urgent palliative radiotherapy for advanced lung cancer: an observational study

BackgroundThere is considerable variability in the management of patients with advanced lung cancer referred for palliative radiotherapy owing to uncertainties in prognosis and the benefit of treatment. This study presents the outcomes of patients seen in the Fast Track Lung Clinic, an urgent access palliative radiotherapy clinic, and aims to identify factors associated with treatment response and survival.MethodsConsecutive patients with advanced lung cancer seen in the Fast Track Lung Clinic between January 2014 and July 2020 were included. Patients who underwent radiotherapy were contacted beginning 30 days after radiotherapy to evaluate treatment response. Cluster bootstraps were used to compute confidence intervals for treatment response rate. Prognostic factors for treatment response and overall survival were identified using multivariable generalized estimating equations and Cox regression models, respectively.ResultsA total of 558 patients were included, of whom 459 (82.3%) consented to palliative radiotherapy for 1053 indications. The overall treatment response rate was 70.0% (95% CI, 65.8-74.2) for indications with follow-up (70.8%). Higher response rates were observed in patients with better ECOG performance status (OR per point, 0.71; 95% CI, 0.55-0.93; P=0.01) and EGFR-mutant non-small cell lung cancer (OR vs wild-type, 2.46; 95% CI, 1.35-4.51; P=0.003), whereas patients treated for neurological symptoms had lower response rates (OR, 0.27; 95% CI, 0.16-0.45; P<0.001). There was no difference in response rate between patients who died within 30 days of starting radiotherapy and those who survived longer (OR, 0.83; 95% CI, 0.42-1.67; P=0.61). Age; ECOG performance status; smoking history; pathology; EGFR or ALK mutation status; and the presence of liver, adrenal, or brain metastases were associated with overall survival.ConclusionsPalliative radiotherapy was effective for patients with advanced lung cancer, although response rates varied by patient characteristics and treatment indication. This study identified prognostic factors for radiotherapy response and overall survival that can inform treatment decisions in this population.

Hypoxia-guided treatment planning for lung cancer with dose painting by numbers.

Tumor hypoxia significantly impacts the efficacy of radiotherapy. Recent developments in the technique of dose painting by numbers (DPBN) promise to improve the tumor control probability (TCP) in conventional radiotherapy for hypoxic cancer. The study initially combined the DPBN method with hypoxia-guided dose distribution optimization to overcome hypoxia for lung cancers and evaluated the effectiveness and appropriateness for clinical use of the DPBN plans. 18F-FMISO PET-CT scans from 13 lung cancer patients were retrospectively employed in our study to make hypoxia-guided radiotherapy. In the clinic, TCP and normal tissue complication probability (NTCP) derived from the DPBN plans in comparison to conventional intensity modulated radiation therapy (IMRT) plans were evaluated. Additionally, in order to investigate the improved clinical suitability, the robustness of DPBN plans in response to potential patient positioning errors and radiation resistance variations throughout the treatment course was assessed. The DPBN approach, employing voxelized prescription doses, led to an average increase of 24.47% in TCP, alongside a reduction of 1.83% in NTCP, compared to the conventional radiotherapy treatment plans. Regarding the robustness of the DPBN plans, it was observed that positional uncertainties were limited to 2mm and radiosensitivity deviations were within 4%. The lung NTCP showed a 0.05% increase when the isocenter was moved by 3mm in any direction, suggesting that the DPBN plan meets clinical acceptability criteria. Our study has shown that the DPBN technique has significant potential as an innovative approach to enhance the efficacy of radiotherapy for lung cancer with hypoxic regions.

The role of radiotherapy in extensive-stage small cell lung cancer: insights from treatment failure patterns in the era of immunotherapy

BackgroundThe therapeutic advantage of radiotherapy (RT) as an adjunct to first-line immunotherapy and chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC) remains unclear. This study aimed to elucidate the value of RT based on the first failure pattern of ES-SCLC.MethodsIn this study, we retrospectively analyzed ES-SCLC patients treated with first-line chemotherapy and immune checkpoint inhibitors (ICIs) at Shanghai Chest Hospital from August 2018 to October 2023. Our study recorded the first failure pattern in ES-SCLC, analyzed the main sites of disease progression, explored factors that may affect prognosis and estimated the value of RT in extending patient survival in the immunotherapy era. Key endpoints included the first failure pattern, progression-free survival (PFS) and overall survival (OS).ResultsAmong 344 patients, 70 (20%) had local failure, 105 (31%) had distant failure, 69 (20%) experienced both types of failure, and 100 (29%) showed no disease progression. Disease progression occurred in 244 patients (71%). They were divided into two groups: 183 without pre-progression RT and 61 with pre-progression RT. In the non-pre-progression RT group, 55 patients (30%) had local failure, 72 patients (39%) had distant failure, and 56 patients (31%) had both. In the pre-progression RT group, 15 patients (25%) had local failure, 33 patients (54%) had distant failure, and 13 patients (21%) had both. Univariate and multivariate analyses identified RT as an independent prognostic factor for improved OS (P < 0.05). Subgroup analysis further confirmed these findings. Pre-progression RT was associated with superior PFS (P < 0.05). The median overall survival (mOS) was 20.1 months (95% confidence interval [CI]: 15.5–24.7 months) in the pre-progression RT group, compared to 13.4 months (95% CI: 13.4–19.2 months) in the non-pre-progression RT group.ConclusionRT improved OS in these patients, and pre-progression RT improved PFS further. Pre-progression RT shows a trend toward OS benefit.

Models and Biomarkers for Local Response Prediction in Early-Stage and Oligometastatic Non-small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Using Machine Learning.

Background A minority of patients receiving stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) are not good responders. Radiomic features can be used to generate predictive algorithms and biomarkers that can determine treatment outcomes and stratify patients to their therapeutic options. This study investigated and attempted to validate the radiomic and clinical features obtained from early-stage and oligometastatic NSCLC patients who underwent SBRT, to predict local response. Methodology A single-institution, Institutional Review Board (IRB)-approved retrospective review was conducted on adult patients with early-stage and oligometastatic SBRT-treated NSCLC at the Jewish General Hospital. The study included 98 patients (82 with early-stage NSCLC and 16 with oligometastatic disease), with a median age of 76 years and a male-to-female ratio of 46:52. A total of 116 lesions were treated with SBRT between 2009 and 2022. Radiomics features (n = 107) were extracted from CT planning scans using PyRadiomics, and clinical data were collected for all 98 patients. Local response was assessed according to Response Evaluation Criteria In Solid Tumors (RECIST 1.1) criteria. Classification models, including support vector machines, random forests, adaptive boosting, and multi-layer perceptrons (MLPs), were used. Models were trained using a fivefold cross-validation scheme. Their performances were measured with receiver operating characteristic plots on the validation folds. Using the importance of the permutation feature, predictive biomarkers were identified. Results The most predictive model, incorporating all patients and using an MLP classifier with Adaptive Synthetic (ADASYN) sampling, a combined-input approach, and a radiomic filter, achieved an area under the curve (AUC) of 0.94 ± 0.05. When oligometastatic patients were omitted, the best model (AUC0.95 ± 0.06) was also predictive, using asupport vector classification (SVC) radial basis function (RBF) classifier, ADASYNsampling, and a clinical-based input. Treatment site and performance status, along with radiomic features such as first-order root-mean-squared-intensity, first-order skewness, and gray-level nonuniformity, were found to be predictive biomarkers. Conclusions The predictive models generated and the biomarkers identified could be used in clinical decision support systems for SBRT-treated NSCLC patients. Additionally, treatment site, performance status, and radiomic features were the most predictive variables.

The Role of Stereotactic Body Radiotherapy in Oligometastatic Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is a major cause of mortality in Canada, with many patients presenting with metastatic disease. The oligometastatic state (OM-NSCLC) may be amenable to cure using aggressive local consolidative therapies. Stereotactic body radiotherapy (SBRT), which entails the utilization of a high dose of radiation in one or few fractions, has many benefits in this setting, including its applicability in varied patient populations to ablate lesions in varied anatomical locations. It has also been demonstrated to prolong the time to next-line systemic therapy, to reduce financial burden, to improve quality-adjusted life years, and reduce adverse events caused by these lesions. This review outlines the published phase II and III trials that have already demonstrated the utility of SBRT in OM-NSCLC, as well as the many ongoing trials aiming to further define its role, including the largest phase II/III trial to date, NRG-LU002. Overall, SBRT appears to improve outcomes when combined with a broad range of standard-of-care therapies and is generally well tolerated; however, careful patient selection is necessary to maximize benefits while minimizing harm. Ongoing trials will help define the optimal patients for SBRT and the best timing for this intervention.