Survival Outcome Analysis of Stereotactic Body Radiotherapy and Immunotherapy (SBRT-IO) versus SBRT-Alone in Unresectable Hepatocellular Carcinoma.

Introduction: While combination of stereotactic body radiotherapy (SBRT) and immunotherapy are promising, their efficacy and safety have not been compared with SBRT-alone in patients with unresectable hepatocellular carcinoma (HCC). Methods: This retrospective study included 100 patients with nonmetastatic, unresectable HCC in two hospitals. Eligible patients had tumor nodules ≤3 and Child-Pugh liver function score of A5 to B7. Seventy patients received SBRT-alone, and 30 patients underwent combined SBRT and immunotherapy (SBRT-IO). Overall survival (OS), time to progression (TTP), overall response rate (ORR), and toxicity were analyzed. We adjusted for the potential confounding factors using propensity score matching. Results: The median tumor size was 7.3 cm (range, 2.6-18 cm). Twenty-five (25%) of patients had vascular invasion. Before propensity score matching, the 1-year and 3-year OS rate was 89.9% and 59.8% in the SBRT-IO group and 75.7% and 42.3% in SBRT-alone group (p = 0.039). After propensity score matching (1:2), 25 and 50 patients were selected from the SBRT-IO and SBRT-alone group. The 1-year and 3-year OS was 92.0% and 63.9% in the SBRT-IO group versus 74.0% and 43.3% in the SBRT-alone group (p = 0.034). The 1-year and 3-year TTP was better in SBRT-IO group (1-year: 68.9% vs. 58.9% and 3-year: 61.3% vs. 32.5%, p = 0.057). The ORR of 88% (complete response [CR]: 56%, partial response [PR]: 22%) in SBRT-IO arm was significantly better than 50% (CR: 20%, PR: 30%) in the SBRT-alone arm (p = 0.006). Three patients (12%) developed ≥grade 3 immune-related treatment adverse events (n = 2 hepatitis, n = 1 dermatitis) leading to permanent treatment discontinuation. Conclusion: Adding immunotherapy to SBRT resulted in better survival with manageable toxicities. Prospective randomized trial is warranted.

Clinical factors and major pathological response after neoadjuvant chemoimmunotherapy in potentially resectable lung squamous cell carcinoma.

Objective: Major pathological response (MPR) helps evaluate the prognosis of patients with lung squamous cell carcinoma (LUSC). However, the clinical factors that affect the achievement of MPR after neoadjuvant chemoimmunotherapy (NCIO) in patients with LUSC remain unclear. This study aimed to explore the clinical factors affecting the MPR after NCIO in patients with potentially resectable LUSC. Methods: This retrospective study included patients with stage IIB-IIIC LUSC who underwent surgical resection after receiving NCIO at a center between March 2020 and November 2022. In addition to the postoperative pathological remission rate, sex, age, body mass index (BMI), smoking history, TNM stage, hematological and imaging test results, and other indicators were examined before NCIO. According to the pathological response rate of the surgically removed tumor tissue, the patients were split into MPR and non-MPR groups. Results: In total, 91 LUSC patients who met the study's eligibility criteria were enrolled: 32 (35%) patients in the non-MPR group and 59 (65%) in the MPR group, which included 43 cases of pathological complete remission (pCR). Pre-treatment lymphocyte level (LY) (odds ratio [OR] =5.997), tumor burden (OR=0.958), N classification (OR=15.915), radiographic response (OR=11.590), pulmonary atelectasis (OR=5.413), and PD-L1 expression (OR=1.028) were independently associated with MPR (all P < 0.05). Based on these six independent predictors, we developed a nomogram model of prediction having an area under the curve (AUC) of 0.914 that is simple to apply clinically to predict the MPR. The MPR group showed greater disease-free survival (DFS) than the non-MPR group, according to the survival analysis (P < 0.001). Conclusion: The MPR rate of NCIO for potentially resectable LUSC was 65%. LY, tumor burden, N classification, radiographic response, pulmonary atelectasis, and PD-L1 expression in patients with LUSC before NCIO were the independent and ideal predictors of MPR. The developed nomogram demonstrated a good degree of accuracy and resilience in predicting the MPR following NCIO, indicating that it is a useful tool for assuring customized therapy for patients with possibly resectable LUSC.

Hypofractionated Stereotactic Radiation Therapy Dosimetric Tolerances for the Inferior Aspect of the Brachial Plexus: A Systematic Review.

We sought to systematically review and summarize dosimetric factors associated with radiation-induced brachial plexopathy (RIBP) after stereotactic body radiation therapy (SBRT) or hypofractionated image guided radiation therapy (HIGRT). From published studies identified from searches of PubMed and Embase databases, data quantifying risks of RIBP after 1- to 10-fraction SBRT/HIGRT were extracted and summarized. Published studies have reported <10% risks of RIBP with maximum doses (Dmax) to the inferior aspect of the brachial plexus of 32 Gy in 5 fractions and 25 Gy in 3 fractions. For 10-fraction HIGRT, risks of RIBP appear to be low with Dmax < 40 to 50 Gy. For a given dose value, greater risks are anticipated with point volume-based metrics (ie, D0.03-0.035cc: minimum dose to hottest 0.03-0.035 cc) versus Dmax. With SBRT/HIGRT, there were insufficient published data to predict risks of RIBP relative to brachial plexus dose-volume exposure. Minimizing maximum doses and possibly volume exposure of the brachial plexus can reduce risks of RIBP after SBRT/HIGRT. Further study is needed to better understand the effect of volume exposure on the brachial plexus and whether there are location-specific susceptibilities along or within the brachial plexus structure.

Treatment-Related Pneumonitis of EGFR Tyrosine Kinase Inhibitors Plus Thoracic Radiation Therapy in Patients With Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Thoracic radiation therapy (RT) for non-small cell lung cancers may overcome resistance to tyrosine kinase inhibitors (TKIs). However, the risk of severe treatment-related pneumonitis (TRP) is a major concern, and the results of the combined treatment remain controversial. Therefore, we aimed to systematically review existing publications and provide a meta-analysis of TRP from a combined therapy of thoracic RT and TKIs. A systematic literature review was performed using the PubMed-MEDLINE and Embase databases to identify eligible publications. The number of severe TRP cases of grade 3 or higher was extracted and then analyzed by fixed or randomized model meta-analysis. Heterogeneity tests were performed using the I² and τ² statistics. Subgroup analyses were conducted on the types of RT and the sequence of the combined treatment. Our literature search identified 37 eligible studies with 1143 patients. Severe TRP occurred in 3.8% (95% CI, 1.8%-6.5%) of patients overall, and fatal pneumonitis occurred rarely in 0.1% (95% CI, 0.0%-0.3%). In the subgroup analysis, the severe TRP proportion was 2.3% (95% CI, 1.0%-4.1%) for patients under definitive (chemo)RT (19 studies, n = 702) versus 2.9% (95% CI, 1.3%-5.1%) for patients who received local stereotactic body RT or palliative RT (15 studies, n = 361). The severe TRP rate was 4.9% (95% CI, 2.4%-8.1%) for concurrent TKI and RT (26 studies, n = 765), which was significantly higher than TRP of 0.4% (95% CI, 0.0%-3.1%) for sequential therapy (6 studies, n = 200). Our meta-analysis showed that combined thoracic RT and epidermal growth factor receptor-TKI therapy has an acceptable risk of severe TRP and rare mortality in patients with non-small cell lung cancers. Concurrent treatment is less tolerable and should be administered with caution. Further investigations using osimertinib are required as the data on its effects are limited.

Dosimetric Effect of Thymus and Thoracic Duct on Radiation-Induced Lymphopenia in Patients With Primary Lung Cancer Who Received Thoracic Radiation.

Purpose: Radiation-induced lymphopenia is a well-recognized factor for tumor control and survival in patients with cancer. This study aimed to determine the role of radiation dose to the thymus and thoracic duct on radiation-induced lymphopenia. Methods and Materials: Patients with primary lung cancer treated with thoracic radiation therapy between May 2015 and February 2020 with whole blood count data were eligible. Clinical characteristics, including age, gender, histology, stage, chemotherapy regimen, radiation dosimetry, and absolute lymphocyte count (ALC) were collected. The thymus and thoracic duct were contoured by one investigator for consistency and checked by one senior physician. The primary endpoint was radiation-induced decrease in lymphocytes, defined as the difference in ALC (DALC) before and after radiation therapy. Results: The data of a total of 116 consecutive patients were retrospectively retrieved. Significant correlations were found between DALC and several clinical factors. These factors include stage, chemotherapy or concurrent chemoradiation, biologically effective dose (BED), mean lung dose, mean body dose, effective dose to immune cells (EDIC), mean thymus dose (MTD), and mean thoracic duct dose (MTDD) (all P < .05). Ridge regression showed that DALC = 0.0063 × BED + 0.0172 × EDIC + 0.0002 × MTD + 0.0147 × MTDD + 0.2510 (overall P = .00025 and F = 5.85). The combination model has the highest area under the curve of 0.77 (P < .001) when fitting the logistic regression model on DALC categorized as binary endpoint. The sensitivity and specificity of the combined model were 89% and 58%, respectively. Conclusions: This study demonstrated for the first time that radiation doses to the thymus and thoracic duct are strongly associated with radiation-induced lymphopenia patients with lung cancer. Further validation studies are needed to implement thymus and thoracic duct as organs at risk.

Efficacy, safety, and correlative biomarkers of bintrafusp alfa in recurrent or metastatic nasopharyngeal cancer patients: a phase II clinical trial.

Background: The strategy of dual blockade of TGF-ß and PD-L1 pathways has not been previously tested in platinum-refractory recurrent or metastatic nasopharyngeal cancer (R/M NPC) patients. This study aimed to evaluate the safety and efficacy of bintrafusp alfa in refractory R/M NPC patients. Methods: In this single-arm, single-centre phase II clinical trial, 38 histologically confirmed R/M NPC patients were enrolled and administered with bintrafusp alfa every 2 weeks. Primary endpoint was objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1). Secondary endpoints included progression-free survival (PFS), overall survival (OS), duration of response (DOR), and safety. Findings: Thirty-eight patients were accrued (33 men; median age, 54 years). ORR was 23.7% (complete response, n = 2; partial response, n = 7). The median DOR was 19.2 months, median PFS was 2.3 months, median OS was 17.0 months, and 1-year OS rate was 63.2%. Unfortunately, 25 patients (65.7%) progressed within 8 weeks of treatment, 15 patients (39.5%) and 8 patients (21.1%) developed hyper-progressive disease (HPD) per RECIST v1.1 and tumor growth rate (TGR) ratio respectively. Sixteen patients (42.4%) experienced ≥ grade 3 treatment-related adverse events (TRAEs), most commonly anemia (n = 9, 23.7%) and secondary malignancies (n = 4, 10.5%). TRAEs led to permanent treatment discontinuation in 7 patients. Patients with strong suppression of plasma TGFß1 level at week 8 were unexpectedly associated with worse ORR (9.1% vs 44.4%, P = 0.046) and development of HPD. There was no correlation between PD-L1 expression and ORR. Interpretation: Bintrafusp alfa demonstrated modest activity in R/M NPC but high rates of HPD and treatment discontinuation secondary to TRAEs are concerning. Funding: The project was supported by Alice Ho Miu Ling Nethersole Charity Foundation Professorship Endowed Fund and Merck KGaA.

The predictive role of soluble programmed death ligand 1 in digestive system cancers.

Introduction: The prognostic role of soluble programmed death ligand 1 (sPD-L1) in digestive system cancers (DSCs) remains inconclusive. This study aimed to explore the predictive value of sPD-L1 expression in DSCs. Methods: Comprehensive searches were run on the electronic databases (PubMed, Web of Science, EMBASE, and the Cochrane Library) to identify studies that assessed the prognostic role of sPD-L1 in DSCs. Review Manager software (version 5.3) was used for all analyses. Pooled data for survival outcomes were measured as hazard ratios (HRs), 95% confidence intervals (CIs), and odds ratios and their 95% CIs. Results: The search identified 18 studies involving 2,070 patients with DSCs. The meta-outcome revealed that a high level of sPD-L1 was related to poorer overall survival (HR, 3.06; 95% CI: 2.22-4.22, p<0.001) and disease-free survival (HR, 2.53; 95% CI: 1.67-3.83, p<0.001) in DSCs. Individually, the prognostic significance of high level of sPD-L1 expression was the highest in hepatic cell carcinoma (HR, 4.76; p<0.001) followed by gastric cancer (HR=3.55, p<0.001). Conclusion: sPD-L1 may be a prognostic factor in DSCs for overall survival and disease-free survival. Inflammatory cytokines, treatment approaches, and other factors may affect the expression of sPD-L1. Therefore, the prognostic value of sPD-L1 for recurrence and metastasis should be further investigated. sPD-L1 may also predict response to treatment. Well-designed prospective studies with standard assessment methods should be conducted to determine the prognostic value of sPD-L1 in DSCs.

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.

PURPOSE: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS: The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Sequential transarterial chemoembolisation and stereotactic body radiotherapy followed by immunotherapy as conversion therapy for patients with locally advanced, unresectable hepatocellular carcinoma (START-FIT): a single-arm, phase 2 trial.

BACKGROUND: The synergy between locoregional therapies and immune checkpoint inhibitors has not been investigated as conversion therapy for unresectable hepatocellular carcinoma. We aimed to investigate the activity of sequential transarterial chemoembolisation (TACE) and stereotactic body radiotherapy followed by avelumab (an anti-PD-L1 drug) for locally advanced, unresectable hepatocellular carcinoma. METHODS: START-FIT was a single-arm, phase 2 trial in patients with locally advanced hepatocellular carcinoma who were not suitable for curative treatment, conducted in two hospitals in Hong Kong and one in Shenzhen, China. Eligible patients were those aged 18 years or older with an Eastern Cooperative Oncology Group performance status 0-1, Child-Pugh liver function score A5 to B7, tumour size of at least 5 cm, a maximum of three tumour lesions, and adequate hepatic, renal, and bone marrow function. Participants received TACE on day 1, followed by stereotactic body radiotherapy (27·5-40·0 Gy in five fractions) at day 28. Avelumab (10 mg/kg) was administered 14 days following stereotactic body radiotherapy and every 2 weeks thereafter. The primary endpoint was the proportion of patients deemed amenable to curative treatment, defined as those who had a sustained complete or partial treatment response for at least 2 months and if curative treatment could be performed (ie, resection, radiofrequency ablation, or transplantation), analysed by intention to treat. Safety was also analysed in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT03817736) and has been completed. FINDINGS: Between March 18, 2019, and Jan 27, 2021, 33 patients (32 [97%] men and one [3%] woman) were enrolled. The median sum of the largest diameters of lesions was 15·1 cm (IQR 8·3-14·9). 21 (64%) patients had macrovascular invasion (hepatic vein [n=13], branched portal vein [n=3], or both [n=5]). Median follow-up was 17·2 months (IQR 7·8-25·8). 18 (55%) patients were deemed amenable to curative treatment: four (12%) of 33 patients had curative treatment (resection [n=2] or radiofrequency ablation [n=2]), and 14 (42%) had a radiological complete response and opted for close surveillance. 11 (33%) of 33 patients had treatment-related adverse events that were grade 3 or worse. The most common treatment-related grade 3 or worse adverse event was transient increase in alanine aminotransferase or aspartate aminotransferase (five [15%]) after TACE. Five (15%) patients developed immune-related adverse events of grade 3 or worse (three had hepatitis, two had dermatitis). INTERPRETATION: To our knowledge, this is the first prospective trial using the combination of immunotherapy and locoregional treatment as conversion therapy for locally advanced unresectable hepatocellular carcinoma, with promising results. Future randomised trials with larger cohorts of patients are warranted. FUNDING: Merck.

Immune related biomarkers for cancer metastasis to the brain.

Brain metastasis accounts for a large number of cancer-related deaths. The host immune system, involved at each step of the metastatic cascade, plays an important role in both the initiation of the brain metastasis and their treatment responses to various modalities, through either local and or systemic effect. However, few reliable immune biomarkers have been identified in predicting the development and the treatment outcome in patients with cancer brain metastasis. Here, we provide a focused perspective of immune related biomarkers for cancer metastasis to the brain and a thorough discussion of the potential utilization of specific biomarkers such as tumor mutation burden (TMB), genetic markers, circulating and tumor-infiltrating immune cells, cytokines, in predicting the brain disease progression and regression after therapeutic intervention. We hope to inspire the field to extend the research and establish practical guidelines for developing and validating immune related biomarkers to provide personalized treatment and improve treatment outcomes in patients with metastatic brain cancers.

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

LY2874455 and Abemaciclib Reverse FGF3/4/19/CCND1 Amplification Mediated Gefitinib Resistance in NSCLC.

Non-small cell lung carcinoma (NSCLC) patients who initially received tyrosine kinase inhibitor (TKI) therapy often acquired resistance via multiple complex mechanisms. The amplification of FGF3/4/19/CCND1 on chromosome 11q13 was found in many cancers with TKI resistance. However, the role of these amplifications in TKI-resistant NSCLC remains uncovered. Here, we generated the FGF3/4/19/CCND1 amplification model in the NSCLC cell lines PC-9 and HCC827. Upregulation of FGF3/4/19/CCND1 strongly promoted cell proliferation and gefitinib resistance in NSCLC cells. To find out the potential therapeutic strategies, we screened the combination of inhibitors against the FGF/FGFR signaling pathway and the CCND1/CDK4 complex and revealed that gefitinib combined with LY2874455 and abemaciclib exhibited the most effective inhibition of resistance in vitro and in vivo. Mechanistically, FGFs/CCND1 activated the MAPK pathway, which was abolished by the combination drugs. Our study provides a rationale for clinical testing of dual targeting FGFR and CCND1 with LY2874455 and abemaciclib in NSCLC patients who harbored FGF3/4/19/CCND1 amplification.

Potential Determinants for Radiation-Induced Lymphopenia in Patients With Breast Cancer Using Interpretable Machine Learning Approach.

Radiation-induced lymphopenia is known for its survival significance in patients with breast cancer treated with radiation therapy. This study aimed to evaluate the impact of radiotherapy on lymphocytes by applying machine learning strategies. We used Extreme Gradient Boosting (XGboost) to predict the event of lymphopenia (grade≥1) and conduced an independent validation. Then, we induced feature attribution analysis (Shapley additive explanation, SHAP) in explaining the XGboost models to explore the directional contribution of each feature to lymphopenia. Finally, we implemented the proof-of-concept clinical validation. The results showed that the XGboost models had rigorous generalization performances (accuracies 0.764 and ROC-AUC 0.841, respectively) in the independent cohort. The baseline lymphocyte counts are the most protective feature (SHAP = 5.226, direction of SHAP = -0.964). Baseline platelets and monocytes also played important protective roles. The usage of taxane only chemotherapy was less risk on lymphopenia than the combination of anthracycline and taxane. By the contribution analysis of dose, we identified that firstly lymphocytes were sensitive to a radiation dose less than 4Gy; secondly the irradiation volume was more important in promoting lymphopenia than the irradiation dose; thirdly the irradiation dose promoted the event of lymphopenia when the irradiation volume was fixed. Overall, our findings paved the way to clarifying the radiation dose volume effect. To avoid radiation-induced lymphopenia, irradiation volume should be kept to a minimum during the planning process, as long as the target coverage is not compromised.

Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy.

PURPOSE: Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). METHODS: A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method. RESULTS: The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. CONCLUSIONS: In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.

Risk Factors for Brain Metastases in Patients With Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.

The use of prophylactic cranial irradiation (PCI) for small cell lung cancer (SCLC) patients is controversial. Risk factors for brain metastasis (BM) development are largely lacking, hampering personalized treatment strategies. This study aimed to identify the possible risk factors for BM in SCLC.We systematically searched the Pubmed database (1 January 1995 to 18 January 2021) according to the PRISMA guidelines. Eligibility criteria: studies reporting detailed BM data with an adequate sample size (randomized clinical trials [RCTs]: N ≥50; non-RCTs: N ≥100) in patients with SCLC. We summarized the reported risk factors and performed meta-analysis to estimate the pooled hazard ratios (HR) if enough qualified data (i.e., two or more studies; the same study type; the same analysis method; and HRs retrievable) were available. In total, 61/536 records were eligible (18 RCTs and 39 non-RCTs comprising 13,188 patients), in which 57 factors were reported. Ten factors qualified BM data for meta-analysis: Limited stage disease (LD) (HR = 0.34, 95% CI: 0.17-0.67; P = 0.002) and older age (≥65) (HR = 0.70, 95% CI: 0.54-0.92; P = 0.01) were associated with less BM; A higher T stage (≥T3) (HR = 1.72, 95% CI: 1.16-2.56; P = 0.007) was a significant risk factor for BM. Male sex (HR = 1.24, 95% CI: 0.99-1.54; P = 0.06) tended to be a risk factor, and better PS (0-1) (HR = 0.66, 95% CI: 0.42-1.02; P = 0.06) tended to have less BM. Smoking, thoracic radiotherapy dose were not significant (P >0.05). PCI significantly decreased BM (P <0.001), but did not improve OS in ED-SCLC (P = 0.81). A higher PCI dose did not improve OS (P = 0.11). The impact on BM was conflicting between Cox regression data (HR = 0.59, 95% CI: 0.26-1.31; P = 0.20) and competing risk regression data (HR = 0.74, 95% CI: 0.55-0.99; P = 0.04). Compared to M0-M1a, M1b was a risk factor for OS (P = 0.01) in ED-SCLC, but not for BM (P = 0.19). As regular brain imaging is rarely performed, high-quality data is lacking. Other factors such as N-stage and blood biomarkers had no qualified data to perform meta-analysis. In conclusion, younger age, higher T stage, and ED are risk factors for BM, suggesting that PCI should be especially discussed in such cases. Individual patient data (IPD) meta-analysis and well-designed RCTs are needed to better identify more risk factors and further confirm our findings. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021228391, identifier CRD42021228391.

Radiation Induced Lymphopenia Is Associated With the Effective Dose to the Circulating Immune Cells in Breast Cancer.

Background: Lymphopenia is a known significant factor for treatment outcome in cancer patients, with underlying risk factor poorly understood in breast cancer. We hypothesize that the effective dose to the circulating immune cells (EDIC) which was related with lymphopenia in lung cancer will also have significant effect for radiation induced lymphopenia (RIL) in patients with breast cancer. Material and Methods: Patients treated with adjuvant radiotherapy (RT) and with complete blood tests within one week from RT end/start (post/preRT) were eligible in this study. Radiation dosimetric factors were collected retrospectively, and EDIC for each patient was calculated based on the doses to lung, heart and total body according to the model description, as previously reported. RIL was defined by the CTCAE5.0 based on postRT peripheral lymphocyte count (PLC). Linear regression was first used to test the correlation between EDIC with post/preRT PLC ratio and postRT PLC, using all these as continuous variables. Normal tissue complication probability (NTCP) was used to develop models that predict the CTCAE graded RIL from EDIC. Results: A total of 735 patients were eligible. The mean post/preRT PLC ratio was 0.66 (95% CI: 0.64-0.68) and mean EDIC of breast cancer was 1.70Gy (95% CI: 1.64-1.75). Both post/preRT PLC ratio and postRT PLC were significantly correlated with EDIC (P<0.001), with R2 of 0.246. For patients with normal preRT PLC, the post/preRT PLC ratio was better associated with EDIC, and postRT PLC was expressed as PLC preRT × (0.89 - 0.16 × EDIC). For patients with preRT lymphopenia, postRT PLC was better associated with EDIC and it was 1.1 - 0.17 × EDIC. Using binned EDIC as the dose variable, the bootstrap validated NTCPs fit the data nicely with R2 of 0.93, 0.96, and 0.94 for grade-1, grade-2, and grade-3 RIL, respectively. The corresponding EDIC to induce 50% of grade-1, grade-2 and grade-3 RIL was 1.2, 2.1 and 3.7 Gy, respectively. Conclusion: EDIC is a significant factor for RIL in patients with breast cancer, and may be used to compute the risk of lymphopenia in each individual patient with the use of the conventional NTCP modeling. External validation is needed before the EDIC can be used to guide RT plan.