Dysfunction of CD8+ T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients.

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

Phase I/II clinical trial of efficacy and safety of EGCG oxygen nebulization inhalation in the treatment of COVID-19 pneumonia patients with cancer.

BACKGROUND: The antiviral drug Nirmatrelvir was found to be a key drug in controlling the progression of pneumonia during the infectious phase of COVID-19. However, there are very few options for effective treatment for cancer patients who have viral pneumonia. Glucocorticoids is one of the effective means to control pneumonia, but there are many adverse events. EGCG is a natural low toxic compound with anti-inflammatory function. Thus, this study was designed to investigate the safety and efficacy of epigallocatechin-3-gallate (EGCG) aerosol to control COVID-19 pneumonia in cancer populations. METHODS: The study was designed as a prospective, single-arm, open-label phase I/II trial at Shandong Cancer Hospital and Institute, between January 5, 2023 to March 31,2023 with viral pneumonia on radiographic signs after confirmed novel coronavirus infection. These patients were treated with EGCG nebulization 10 ml three times daily for at least seven days. EGCG concentrations were increased from 1760-8817umol/L to 4 levels with dose escalation following a standard Phase I design of 3-6 patients per level. Any grade adverse event caused by EGCG was considered a dose-limiting toxicity (DLT). The maximum tolerated dose (MTD) is defined as the highest dose with less than one-third of patients experiencing dose limiting toxicity (DLT) due to EGCG. The primary end points were the toxicity of EGCG and CT findings, and the former was graded by Common Terminology Criteria for Adverse Events (CTCAE) v. 5.0. The secondary end point was the laboratory parameters before and after treatment. RESULT: A total of 60 patients with high risk factors for severe COVID-19 pneumonia (factors such as old age, smoking and combined complications)were included in this phase I-II study. The 54 patients in the final analysis were pathologically confirmed to have tumor burden and completed the whole course of treatment. A patient with bucking at a level of 1760 umol/L and no acute toxicity associated with EGCG has been reported at the second or third dose gradients. At dose escalation to 8817umol/L, Grade 1 adverse events of nausea and stomach discomfort occurred in two patients, which resolved spontaneously within 1 hour. After one week of treatment, CT showed that the incidence of non-progression of pneumonia was 82% (32/39), and the improvement rate of pneumonia was 56.4% (22/39). There was no significant difference in inflammation-related laboratory parameters (white blood cell count, lymphocyte count, IL-6, ferritin, C-reactive protein and lactate dehydrogenase) before and after treatment. CONCLUSION: Aerosol inhalation of EGCG is well tolerated, and preliminary investigation in cancer population suggests that EGCG may be effective in COVID-19-induced pneumonia, which can promote the improvement of patients with moderate pneumonia or prevent them from developing into severe pneumonia. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05758571. Date of registration: 8 February 2023.

Clinical characteristics and MRI based radiomics nomograms can predict iPFS and short-term efficacy of third-generation EGFR-TKI in EGFR-mutated lung adenocarcinoma with brain metastases.

BACKGROUND: Predicting short-term efficacy and intracranial progression-free survival (iPFS) in epidermal growth factor receptor gene mutated (EGFR-mutated) lung adenocarcinoma patients with brain metastases who receive third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapy was of great significance for individualized treatment. We aimed to construct and validate nomograms based on clinical characteristics and magnetic resonance imaging (MRI) radiomics for predicting short-term efficacy and intracranial progression free survival (iPFS) of third-generation EGFR-TKI in EGFR-mutated lung adenocarcinoma patients with brain metastases. METHODS: One hundred ninety-four EGFR-mutated lung adenocarcinoma patients with brain metastases who received third-generation EGFR-TKI treatment were included in this study from January 1, 2017 to March 1, 2023. Patients were randomly divided into training cohort and validation cohort in a ratio of 5:3. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) regression. Logistic regression analysis and Cox proportional hazards regression analysis were used to screen clinical risk factors. Single clinical (C), single radiomics (R), and combined (C + R) nomograms were constructed in short-term efficacy predicting model and iPFS predicting model, respectively. Prediction effectiveness of nomograms were evaluated by calibration curves, Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to compare the iPFS of high and low iPFS rad-score patients in the predictive iPFS R model and to compare the iPFS of high-risk and low-risk patients in the predictive iPFS C + R model. RESULTS: Overall response rate (ORR) was 71.1%, disease control rate (DCR) was 91.8% and median iPFS was 12.67 months (7.88-20.26, interquartile range [IQR]). There were significant differences in iPFS between patients with high and low iPFS rad-scores, as well as between high-risk and low-risk patients. In short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.867 (0.835-0.900, 95%CI) and 0.803 (0.753-0.854, 95%CI), while in iPFS model, the C-indexes were 0.901 (0.874-0.929, 95%CI) and 0.753 (0.713-0.793, 95%CI). CONCLUSIONS: The third-generation EGFR-TKI showed significant efficacy in EGFR-mutated lung adenocarcinoma patients with brain metastases, and the combined line plot of C + R can be utilized to predict short-term efficacy and iPFS.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

OBJECTIVES: To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. METHODS: Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. RESULTS: The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. CONCLUSIONS: A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. ADVANCES IN KNOWLEDGE: The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

The analysis of multiple omics and examination of pathological images revealed the prognostic and therapeutic significances of CD93 in lung squamous cell carcinoma.

As a potent pro-angiogenic factor, the role of CD93 in the prognosis and therapeutic outcomes of lung squamous cell carcinoma (LUSC) merits exploration. In this study, we systematically collected transcriptomic, genomic, and clinical data from various public databases, as well as pathological images from hospital-operated patients. Employing statistical analysis software like R (Version 4.2.2) and GraphPad (Version 8.0), we conducted comprehensive analyses of multi-omics data. The results revealed elevated CD93 expression in LUSC tissues, closely associated with various cancer-related pathways. High CD93 expression indicated advanced clinical stage and poorer prognosis. Furthermore, CD93 contributed to resistance against chemotherapy and immunotherapy by enhancing tumor cell stemness, reducing immune cell infiltration, and inducing T cell exhaustion. Patients with low CD93 expression exhibited higher response rates to both chemotherapy and immunotherapy. Immunohistochemistry validated the significance of CD93 in LUSC. CD93 emerges as a biomarker signaling unfavorable prognosis and influencing therapeutic outcomes, suggesting a potential LUSC treatment avenue.

Feasibility evaluation of radiotherapy positioning system guided by augmented reality and point cloud registration.

PURPOSE: To develop a radiotherapy positioning system based on Point Cloud Registration (PCR) and Augmented Reality (AR), and to verify its feasibility. METHODS: The optimal steps of PCR were investigated, and virtual positioning experiments were designed to evaluate its accuracy and speed. AR was implemented by Unity 3D and Vuforia for initial position correction, and PCR for precision registration, to achieve the proposed radiotherapy positioning system. Feasibility of the proposed system was evaluated through phantom positioning tests as well as real human positioning tests. Real human tests involved breath-holding positioning and free-breathing positioning tests. Evaluation metrics included 6 Degree of Freedom (DOF) deviations and Distance (D) errors. Additionally, the interaction between CBCT and the proposed system was envisaged through CBCT and optical cross-source PCR. RESULTS: Point-to-plane iterative Closest Point (ICP), statistical filtering, uniform down-sampling, and optimal sampling ratio were determined for PCR procedure. In virtual positioning tests, a single registration took only 0.111 s, and the average D error for 15 patients was 0.015 ± 0.029 mm., Errors of phantom tests were at the sub-millimeter level, with an average D error of 0.6 ± 0.2 mm. In the real human positioning tests, the average accuracy of breath-holding positioning was still at the sub-millimeter level, where the errors of X, Y and Z axes were 0.59 ± 0.12 mm, 0.54 ± 0.12 mm, and 0.52 ± 0.09 mm, and the average D error was 0.96 ± 0.22 mm. In the free-breathing positioning, the average errors in X, Y, and Z axes were still less than 1 mm. Although the mean D error was 1.93 ± 0.36 mm, it still falls within a clinically acceptable error margin. CONCLUSION: The AR and PCR-guided radiotherapy positioning system enables markerless, radiation-free and high-accuracy positioning, which is feasible in real-world scenarios.

Spatial features of specific CD103+CD8+ tissue-resident memory T cell subsets define the prognosis in patients with non-small cell lung cancer.

BACKGROUND: Tissue-resident memory T (TRM) cells can reside in the tumor microenvironment and are considered the primary response cells to immunotherapy. Heterogeneity in functional status and spatial distribution may contribute to the controversial role of TRM cells but we know little about it. METHODS: Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of TRM cell subsets were recognized in the tissue from 274 patients with NSCLC after radical surgery. By integrating multiple machine learning methods, we constructed a TRM-based spatial immune signature (TRM-SIS) to predict the prognosis. Furthermore, we conducted a CD103-related gene set enrichment analysis (GSEA) and verified its finding by another mIF panel (CD8, CD103, CK, CD31, Hif-1α). RESULTS: The density of TRM cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of TRM cell subsets was defined, including TRM1 (PD-1-Tim-3-TRM), TRM2 (PD-1+Tim-3-TRM), TRM3 (PD-1-Tim-3+TRM) and TRM4 (PD-1+Tim-3+TRM). The cytotoxicity of TRM2 was the strongest while that of TRM4 was the weakest. Compare with TRM1 and TRM2, TRM3 and TRM4 had better infiltration and stronger interaction with cancer cells. The TRM-SIS was an independent prognostic factor for disease-free survival [HR = 2.43, 95%CI (1.63-3.60), P < 0.001] and showed a better performance than the TNM staging system for recurrence prediction. Furthermore, by CD103-related GSEA and mIF validation, we found a negative association between tumor angiogenesis and infiltration of TRM cells. CONCLUSIONS: These findings reveal a significant heterogeneity in the functional status and spatial distribution of TRM cells, and support it as a biomarker for the prognosis of NSCLC patients. Regulating TRM cells by targeting tumor angiogenesis may be a potential strategy to improve current immunotherapy.

A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study.

OBJECTIVES: Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT. METHODS: Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups. RESULTS: In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001). CONCLUSIONS: The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non-Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial.

Importance: Adjuvant and neoadjuvant immunotherapy have improved clinical outcomes for patients with early-stage non-small cell lung cancer (NSCLC). However, the optimal combination of checkpoint inhibition with chemotherapy remains unknown. Objective: To determine whether toripalimab in combination with platinum-based chemotherapy will improve event-free survival and major pathological response in patients with stage II or III resectable NSCLC compared with chemotherapy alone. Design, Setting, and Participants: This randomized clinical trial enrolled patients with stage II or III resectable NSCLC (without EGFR or ALK alterations for nonsquamous NSCLC) from March 12, 2020, to June 19, 2023, at 50 participating hospitals in China. The data cutoff date for this interim analysis was November 30, 2022. Interventions: Patients were randomized in a 1:1 ratio to receive 240 mg of toripalimab or placebo once every 3 weeks combined with platinum-based chemotherapy for 3 cycles before surgery and 1 cycle after surgery, followed by toripalimab only (240 mg) or placebo once every 3 weeks for up to 13 cycles. Main Outcomes and Measures: The primary outcomes were event-free survival (assessed by the investigators) and the major pathological response rate (assessed by blinded, independent pathological review). The secondary outcomes included the pathological complete response rate (assessed by blinded, independent pathological review) and adverse events. Results: Of the 501 patients randomized, 404 had stage III NSCLC (202 in the toripalimab + chemotherapy group and 202 in the placebo + chemotherapy group) and 97 had stage II NSCLC and were excluded from this interim analysis. The median age was 62 years (IQR, 56-65 years), 92% of patients were male, and the median follow-up was 18.3 months (IQR, 12.7-22.5 months). For the primary outcome of event-free survival, the median length was not estimable (95% CI, 24.4 months-not estimable) in the toripalimab group compared with 15.1 months (95% CI, 10.6-21.9 months) in the placebo group (hazard ratio, 0.40 [95% CI, 0.28-0.57], P < .001). The major pathological response rate (another primary outcome) was 48.5% (95% CI, 41.4%-55.6%) in the toripalimab group compared with 8.4% (95% CI, 5.0%-13.1%) in the placebo group (between-group difference, 40.2% [95% CI, 32.2%-48.1%], P < .001). The pathological complete response rate (secondary outcome) was 24.8% (95% CI, 19.0%-31.3%) in the toripalimab group compared with 1.0% (95% CI, 0.1%-3.5%) in the placebo group (between-group difference, 23.7% [95% CI, 17.6%-29.8%]). The incidence of immune-related adverse events occurred more frequently in the toripalimab group. No unexpected treatment-related toxic effects were identified. The incidence of grade 3 or higher adverse events, fatal adverse events, and adverse events leading to discontinuation of treatment were comparable between the groups. Conclusions and Relevance: The addition of toripalimab to perioperative chemotherapy led to a significant improvement in event-free survival for patients with resectable stage III NSCLC and this treatment strategy had a manageable safety profile. Trial Registration: ClinicalTrials.gov Identifier: NCT04158440.

Body Composition and Radiomics From 18 F-FDG PET/CT Together Help Predict Prognosis for Patients With Stage IV Non-Small Cell Lung Cancer.

PURPOSE: To determine whether integration of data on body composition and radiomic features obtained using baseline 18 F-FDG positron emission tomography/computed tomography (PET/CT) images can be used to predict the prognosis of patients with stage IV non-small cell lung cancer (NSCLC). METHODS: A total of 107 patients with stage IV NSCLC were retrospectively enrolled in this study. We used the 3D Slicer (The National Institutes of Health, Bethesda, Maryland) software to extract the features of PET and CT images. Body composition measurements were taken at the L3 level using the Fiji (Curtis Rueden, Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison) software. Independent prognostic factors were defined by performing univariate and multivariate analyses for clinical factors, body composition features, and metabolic parameters. Data on body composition and radiomic features were used to build body composition, radiomics, and integrated (combination of body composition and radiomic features) nomograms. The models were evaluated to determine their prognostic prediction capabilities, calibration, discriminatory abilities, and clinical applicability. RESULTS: Eight radiomic features relevant to progression-free survival (PFS) were selected. Multivariate analysis showed that the visceral fat area/subcutaneous fat area ratio independently predicted PFS ( P = 0.040). Using the data for body composition, radiomic features, and integrated features, nomograms were established for the training (areas under the curve = 0.647, 0.736, and 0.803, respectively) and the validation sets (areas under the receiver operating characteristic = 0.625, 0.723, and 0.866, respectively); the integrated model showed better prediction ability than that of the other 2 models. The calibration curves revealed that the integrated nomogram exhibited a better agreement between the estimation and the actual observation in terms of prediction of the probability of PFS than that of the other 2 models. Decision curve analysis revealed that the integrated nomogram was superior to the body composition and radiomics nomograms for predicting clinical benefit. CONCLUSION: Integration of data on body composition and PET/CT radiomic features can help in prediction of outcomes in patients with stage IV NSCLC.

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks. METHODS: To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights. RESULTS: The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization. CONCLUSION: With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.

Dynamic changes in the suitable areas for the pinewood nematode in the Sichuan-Chongqing Region of China.

The pine wood nematode (PWN), one of the largest alien forestry pests in China, has caused numerous deaths of conifer forests in Europe and Asia, and is spreading to other suitable areas worldwide. Information on the spatial distribution of the PWN can provide important information for the management of this species. Here, the current and future geographical distributions of PWN were simulated in the Sichuan-Chongqing region of China in detail based on the MaxEnt model. The results indicated excellent prediction performance, with an area under curve score of more than 0.9. The key factors selected were the altitude, maximum temperature of the warmest month, annual precipitation, precipitation of the wettest quarter, and minimum temperature of the coldest month, with thresholds of < 400 m, > 37.5 °C, 1100-1250 mm, 460-530 mm and > 4.0 °C, respectively, indicating that the PWN can live in low-altitude, warm, and humid areas. The suitable region for the PWN is mainly concentrated in the metropolitan area, northeast of Chongqing, and the southeastern and eastern parts of Sichuan Province. Most importantly, in addition to their actual distribution area, the newly identified suitably distribution areas A, B, C, and D for the coming years and E, F, G, and H for the period-2041-2060 (2050s) should be strictly monitored for the presence of PWNs. Altogether, the suitable distribution ranges of the PWN in the Sichuan-Chongqing region show an increasing trend; therefore, owing to its inability to disperse by itself, human activities involving pine trees and vectors of the Japanese pine sawyer should be intensively controlled to prevent the PWN from spreading to these newly discovered suitable areas.

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model.

As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues.

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

Efficacy and safety of epigallocatechin-3-gallate in treatment acute severe dermatitis in patients with cancer receiving radiotherapy: a phase I clinical trial.

To evaluate the safety and effectiveness of epigallocatechin-3-gallate (EGCG) solution treating the acute severe dermatitis in patients receiving radiotherapy. This phase I research enrolled patients with thoracic cancer receiving radiotherapy at Shandong Cancer Hospital and Institute in Shandong, China. EGCG solution was sprayed to the radiation field when grade III radiation-induced dermatitis (RID) first appearance. EGCG concentration escalated from 660 to 2574 µmol/L using modified-Fibonacci dose-escalation. RID and related symptoms were followed up every day. Between March 2021 and November 2021, 19 patients were enrolled in this phase I research. The median dose of grade III RID first observation was 44 Gy (30.6-52 Gy). As the EGCG treatment was performed continuously, all these grade III RID reactions were significantly decreased to grade I or grade II RID at three days after use of EGCG (p < 0.001). Significant relief can be observed in burning sensation (p < 0.001), tractive sensation (p < 0.001), tenderness (p < 0.001), erythema (p < 0.001), itching (p < 0.001) and pain (p < 0.001) after 15 days of EGCG treatment. No radiation therapy delay or interruption for all 19 patients. No adverse events were observed and reported associated with EGCG. The highest dose of this Phase I trial (2574 µmol/L) was recommended for continuous Phase II trial for further evaluation. In this phase I clinical research, use of EGCG solution is safe and can significantly relief grade III RID in patients receiving radiotherapy. Thus, EGCG might be a new choice for acute sever RID.Trial Registration: ClinicalTrials.gov Identifier: NCT02580279 (Full date of first registration: 12/2014).

18F-FDG PET/CT radiomics for prediction of lymphovascular invasion in patients with early stage non-small cell lung cancer.

Objective: To explore a prediction model for lymphovascular invasion (LVI) on cT1-2N0M0 radiologic solid non-small cell lung cancer (NSCLC) based on a 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography-computed tomography (PET-CT) radiomics analysis. Methods: The present work retrospectively included 148 patients receiving surgical resection and verified pathologically with cT1-2N0M0 radiologic solid NSCLC. The cases were randomized into training or validation sets in the ratio of 7:3. PET and CT images were used to select optimal radiomics features. Three radiomics predictive models incorporating CT, PET, as well as PET/CT images radiomics features (CT-RS, PET-RS, PET/CT-RS) were developed using logistic analyses. Furthermore, model performance was evaluated by ROC analysis for predicting LVI status. Model performance was evaluated in terms of discrimination, calibration along with clinical utility. Kaplan-Meier curves were employed to analyze the outcome of LVI. Results: The ROC analysis demonstrated that PET/CT-RS (AUCs were 0.773 and 0.774 for training and validation sets) outperformed both CT-RS(AUCs, 0.727 and 0.752) and PET-RS(AUCs, 0.715 and 0.733). A PET/CT radiology nomogram (PET/CT-model) was developed to estimate LVI; the model demonstrated conspicuous prediction performance for training (C-index, 0.766; 95%CI, 0.728-0.805) and validation sets (C-index, 0.774; 95%CI, 0.702-0.846). Besides, decision curve analysis and calibration curve showed that PET/CT-model provided clinically beneficial effects. Disease-free survival and overall survival varied significantly between LVI and non-LVI cases (P<0.001). Conclusions: The PET/CT radiomics models could effectively predict LVI on early stage radiologic solid lung cancer and provide support for clinical treatment decisions.

18F-FDG PET/CT radiomics predicts brain metastasis in I-IIIA resected Non-Small cell lung cancer.

OBJECTIVE: To establish 18F-FDG PET/CT radiomics model for predicting brain metastasis in non-small cell lung cancer (NSCLC) patients. METHODS: This research comprised 203 NSCLC patients who had received surgical therapy at two institutions. To identify independent predictive factors of brain metastasis, metabolic indicators, CT features, and clinical features were investigated. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables. The suggested model's performance was assessed from the perspective of discrimination, calibration, and clinical application. RESULTS: The C-indices of the PET/CT radiomics model in the training, internal validation, and external validation cohorts were 0.911, 0.825 and 0.800, respectively. According to the multivariate analysis, neuron-specific enolase (NSE) and air bronchogram were independent risk factors for brain metastasis (BM). Furthermore, the combined model integrating radiomics and clinicopathological characteristics related to brain metastasis performed better in terms of prediction, with C-indices of 0.927, 0.861, and 0.860 in the training, internal validation, and external validation cohorts, respectively. The decision curve analysis (DCA) suggested that the PET/CT nomogram was clinically beneficial. CONCLUSIONS: A predictive algorithm based on PET/CT imaging information and clinicopathological features may accurately predict the probability of brain metastasis in NSCLC patients following surgery. This presented doctors with a unique technique for screening NSCLC patients at high risk of brain metastasis.

Five-year follow-up after stereotactic body radiotherapy for medically inoperable early-stage non-small cell lung cancer: a multicenter study.

Background: Stereotactic body radiotherapy (SBRT) has proven to provide high rates of tumor control for patients with early-stage non-small cell lung cancer (NSCLC). We are reporting a multicenter experience of long-term clinical outcomes and adverse effect profiles of patients with medically inoperable early-stage NSCLC treated with SBRT. Methods: A total of 145 early-stage NSCLC patients underwent SBRT at the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Shandong Cancer Hospital and Institute, and Shanghai Pulmonary Hospital between October 2012 and March 2019. Four-dimensional computed tomography (4D-CT) simulation was used for all patients. All received a biologically effective dose (BED; α/ß=10) of 96-120 Gy with the prescribed isodose line covering >95% of the planning target volume (PTV). Survival was analyzed by the Kaplan-Meier method. Survival was estimated using the Kaplan-Meier method. Results: The median tumor diameter was 2.2 (range, 0.5-5.2) cm. The median follow-up was of 65.6 months. Thirty-five patients (24.1%) developed disease recurrence. The rates of local, regional, and distant disease recurrence were, respectively, 5.1%, 7.4%, and 13.2% at 3 years; and 9.6%, 9.8%, and 15.8% at 5 years. Progression-free survival (PFS) rates at 3 and 5 years were 69.2% and 60.5% respectively; the overall survival (OS) rates were 78.1% and 70.1%, respectively. Five patients (3.4%) experienced grade 3 treatment-related adverse events (AEs). No patient experienced grade 4 or 5 toxicity. Conclusions: From our retrospective analysis with long-term follow-up in Chinese population, SBRT achieved high rate of local control (LC) and low toxicity in patients with early-stage NSCLC. This study offered robust long-term outcome data of SBRT in the Chinese population, which was very rarely reported in China before.

Efficacy and safety of immunoradiotherapy for advanced non-small cell lung cancer: a retrospective comparative cohort study.

Background: Treatment of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) may remarkably improve the prognosis in patients with metastatic non-small cell lung cancer (NSCLC). However, the treatment time of RT, irradiated lesion and the optimum combined scheme, have not been fully determined. Methods: Data regarding overall survival (OS), progression-free survival (PFS), treatment response, and adverse events of 357 patients with advanced NSCLC treated with ICI alone or in combination with RT prior to/during ICI treatment were retrospectively collected. Additionally, subgroup analyses for radiation dose, time interval between RT and immunotherapy, and number of irradiated lesions were performed. Results: Median PFS of the ICI alone and ICI + RT groups was 6 and 12 months, respectively (P<0.0001). The objective response rate (ORR) and disease control rate (DCR) were significantly higher in the ICI + RT group than in the ICI alone group (P=0.014; P=0.015, respectively). However, OS, the distant response rate (DRR), and the distant control rate (DCRt) did not differ significantly between the groups. Out-of-field DRR and DCRt were defined in unirradiated lesions only. Compared with RT application prior to ICI, its application concomitant with ICI led to higher DRR (P=0.018) and DCRt (P=0.002). Subgroup analyses revealed that single-site, high biologically effective dose (BED) (≥72 Gy), planning target volume (PTV) size (<213.7 mL) RT groups had better PFS. In multivariate analysis, PTV volume [≥213.7 vs. <213.7 mL: hazard ratio (HR), 1.89; 95% confidence interval (CI): 1.04-3.42; P=0.035] was an independent predictor of immunotherapy PFS. Additionally, radio-immunotherapy increased the incidence rate of grade 1-2 immune-related pneumonitis compared with ICI alone. Conclusions: Combination therapy using ICIs and radiation may improve PFS and tumor response rates in advanced NSCLC patients regardless of programmed cell death 1 ligand 1 (PD-L1) level or previous treatments. However, it may increase the incidence of immune-related pneumonitis.

Characterization and clinical verification of immune-related genes in hepatocellular carcinoma to aid prognosis evaluation and immunotherapy.

BACKGROUND: Immune-related genes (IRGs) have been confirmed to play an important role in tumorigenesis and tumor microenvironment formation in hepatocellular carcinoma (HCC). We investigated how IRGs regulates the HCC immunophenotype and thus affects the prognosis and response to immunotherapy. METHODS: We investigated RNA expression of IRGs and developed an immune-related genes-based prognostic index (IRGPI) in HCC samples. Then, the influence of the IRGPI on the immune microenvironment was comprehensively analysed. RESULTS: According to IRGPI, HCC patients are divided into two immune subtypes. A high IRGPI was characterized by an increased tumor mutation burden (TMB) and a poor prognosis. More CD8 + tumor infiltrating cells and expression of PD-L1 were observed in low IRGPI subtypes. Two immunotherapy cohorts confirmed patients with low IRGPI demonstrated significant therapeutic benefits. Multiplex immunofluorescence staining determined that there were more CD8 + T cells infiltrating into tumor microenvironment in IRGPI-low groups, and the survival time of these patients was longer. CONCLUSIONS: This study demonstrated that the IRGPI serve as a predictive prognostic biomarker and potential indicator for immunotherapy.