Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-ß) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life.

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

Objective: The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods: This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results: 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion: This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma.

BACKGROUND & AIMS: The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. METHODS: We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. RESULTS: Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively). CONCLUSION: CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Dosimetric comparison of helical tomotherapy and volumetric modulated arc therapy in hippocampal avoidance whole-brain radiotherapy.

OBJECTIVE: This study aimed to discuss the dosimetric advantages of helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT) technology in hippocampal avoidance whole-brain radiotherapy and provide references for clinical selection of ideal radiotherapy technology. METHODS: A total of 20 patients with hippocampal avoidance whole-brain radiotherapy were chosen randomly. Computed tomography (CT) and MRI scanning images were input into the treatment planning system (TPS). After the CT and enhanced magnetic resonance T1 weighted images were fused and registered, the same radiation therapy physician was invited to outline the tumor target volume. PTV-HS refers to the whole brain subtracted by 5 mm outward expansion of the hippocampus (HP). The prescribed dose was 30 Gy/10 fractions. HT and VMAT plans were designed for each patient in accordance with PTV. Under the premise that the 95% isodose curve covers the PTV, dose-volume histogram was applied to evaluate the PTV, conformal index (CI), heterogeneity index (HI), maximum dose (Dmax), mean dose (Dmean), minimum dose (Dmin) and absorbed doses of organs at risk (OARs) in HT and VMAT plans. Paired t-test was performed to compare the differences between two radiation therapy plans, and p  <  0.05 was considered statistically significant. RESULTS: These two plans had no significant difference in PTV-HS (max, min, and mean). However, the HI and CI of the HT plan were significantly better than those of the VMAT plan, showing statistically significant difference (p < 0.05). The HT plan was significantly superior to the VMAT plan in terms of the Dmax, Dmin, and Dmean of HP, left and right eye lens, left and right eye, and spinal cord, showing statistically significant difference (p < 0.05). The HT plan was also better than the VMAT plan in terms of the Dmax of the left optic nerve. However, the two plans showed no obvious differences in terms of the absorbed doses of the right optic nerve and brainstem, without statistical significance. CONCLUSIONS: Compared with the VMAT plan of hippocampal avoidance, HT technology has significant dosimetric advantages. HT plans significantly decreased the radiation dose and radiation volume of OARs surrounding the target area (e.g., surrounding eye lens and eye, especially hippocampal avoidance area) while increasing the CI and HI of PTV dose in whole brain radiotherapy (WBRT) greatly, thus enabling the decrease in the incidence rate of radioactive nerve function impairment.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.

BACKGROUND: Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. METHODS: Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. RESULTS: The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. CONCLUSION: Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images.

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Depicting and predicting changes of lung after lobectomy for cancer by using CT images.

Lobectomy is an effective and well-established therapy for localized lung cancer. This study aimed to assess the lung and lobe change after lobectomy and predict the postoperative lung volume. The study included 135 lung cancer patients from two hospitals who underwent lobectomy (32, right upper lobectomy (RUL); 31, right middle lobectomy (RML); 24, right lower lobectomy (RLL); 26, left upper lobectomy (LUL); 22, left lower lobectomy (LLL)). We initially employ a convolutional neural network model (nnU-Net) for automatically segmenting pulmonary lobes. Subsequently, we assess the volume, effective lung volume (ELV), and attenuation distribution for each lobe as well as the entire lung, before and after lobectomy. Ultimately, we formulate a machine learning model, incorporating linear regression (LR) and multi-layer perceptron (MLP) methods, to predict the postoperative lung volume. Due to the physiological compensation, the decreased TLV is about 10.73%, 8.12%, 13.46%, 11.47%, and 12.03% for the RUL, RML, RLL, LUL, and LLL, respectively. The attenuation distribution in each lobe changed little for all types of lobectomy. LR and MLP models achieved a mean absolute percentage error of 9.8% and 14.2%, respectively. Radiological findings and a predictive model of postoperative lung volume might help plan the lobectomy and improve the prognosis.

Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images.

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation .

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.

Background: Diagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC. Methods: Two gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Results: A total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set. Conclusions: Several potential gene signatures associated with NPC were identified, and a high-performance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

Reducing Target Volumes of Intensity Modulated Radiation Therapy After Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma: Long-Term Results of a Prospective, Multicenter, Randomized Trial.

PURPOSE: The objective of this study was to estimate the long-term survival, late toxicity profile, and quality of life of patients with locoregionally advanced nasopharyngeal carcinoma (NPC) treated with combined induction chemotherapy (IC) and concurrent chemoradiotherapy from a clinical trial focused on reducing the target volume of intensity modulated radiation therapy (IMRT). METHODS AND MATERIALS: This prospective, randomized clinical trial was conducted across 6 Chinese hospitals and included 212 patients with stage III-IVB NPC who were randomly allocated to a pre-IC or post-IC group. Eligible patients were treated with 2 cycles of IC + CCRT. All patients underwent radical IMRT. Gross tumor volumes of the nasopharynx were delineated according to pre-IC and post-IC tumor extent in the pre-IC and post-IC groups, respectively. RESULTS: After a median follow-up of 98.4 months, 32 of 97 (32.9%) and 33 of 115 (28.7%) patients experienced treatment failure or died in the pre-IC and post-IC groups, respectively. None of the patients developed grade 4 late toxicity. Late radiation-induced toxicity predominantly manifested as grade 1 to 2 subcutaneous fibrosis, hearing loss, tinnitus, and xerostomia, whereas grade 3 late toxicity included xerostomia and hearing loss. The 5-year estimated overall, progression-free, locoregional recurrence-free, and distant metastasis-free survival rates in the pre-IC and post-IC groups were 78.2% versus 83.3%, 72.0% versus 78.1%, 90.2% versus 93.5%, and 78.1% versus 82.1%, respectively. The pre-IC group had a significantly higher incidence of xerostomia and hearing damage than the post-IC group. In terms of quality of life, compared with the pre-IC group, the post-IC group showed significant improvement in cognitive function (P = .045) and symptoms including dry mouth (P = .004), sticky saliva (P = .047), and feeling ill (P = .041). CONCLUSIONS: After long-term follow-up, we confirmed that reducing the target volumes of IMRT after IC in locoregionally advanced NPC showed no inferiority in terms of the risk of locoregional relapse and potentially improved quality of life and alleviated late toxicity.

Risk Factors for Operation Complications of High Dose Rate 3-Dimensional Interstitial Brachytherapy for Lung Cancer.

BACKGROUND: The risk factors for operation complications of high-dose-rate dimensional (3D) interstitial brachytherapy for lung malignant tumors are still unclear. We aimed to provide a reliable reference for the preoperative safety assessment of interstitial brachytherapy. PATIENTS AND METHODS: We analyzed the degree and incidence of operational complications in 120 eligible patients with lung carcinoma who underwent computed tomography (CT)-guided HDR interstitial brachytherapy. Univariate and multivariate analyses were used to study the relationships between patient-related factors, tumor-related factors, operation-related factors, and operational complications. RESULTS: The most frequent complications of CT-guided HDR interstitial brachytherapy were pneumothorax and hemorrhage. In univariate analysis, smoking, emphysema, distance of implanted needles through the normal lung tissue, number of implanted needle adjustments, and distance of the lesion from the pleura were the risk factors for pneumothorax; the tumor size, distance of the tumor from the pleura, number of implanted needle adjustments, and distance of the implanted needle through the normal lung tissue were risk factors for hemorrhage. In multivariate analysis, the depth of the implanted needle through the normal lung tissue and distance of the lesion from the pleura were independent risk factors for pneumothorax. Tumor size, number of implanted needle adjustments, and distance through normal lung tissue were independent risk factors for hemorrhage. CONCLUSION: This study provides a reference for the clinical treatment of lung cancer by analyzing the risk factors for complications of interstitial brachytherapy.

Dose prediction of organs at risk in patients with cervical cancer receiving brachytherapy using needle insertion based on a neural network method.

OBJECTIVE: A neural network method was employed to establish a dose prediction model for organs at risk (OAR) in patients with cervical cancer receiving brachytherapy using needle insertion. METHODS: A total of 218 CT-based needle-insertion brachytherapy fraction plans for loco-regionally advanced cervical cancer treatment were analyzed in 59 patients. The sub-organ of OAR was automatically generated by self-written MATLAB, and the volume of the sub-organ was read. Correlations between D2cm3 of each OAR and volume of each sub-organ-as well as high-risk clinical target volume for bladder, rectum, and sigmoid colon-were analyzed. We then established a neural network predictive model of D2cm3 of OAR using the matrix laboratory neural net. Of these plans, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression R value and mean squared error were subsequently used to evaluate the predictive model. RESULTS: The D2cm3/D90 of each OAR was related to volume of each respective sub-organ. The R values for bladder, rectum, and sigmoid colon in the training set for the predictive model were 0.80513, 0.93421, and 0.95978, respectively. The ∆D2cm3/D90 for bladder, rectum, and sigmoid colon in all sets was 0.052 ± 0.044, 0.040 ± 0.032, and 0.041 ± 0.037, respectively. The MSE for bladder, rectum, and sigmoid colon in the training set for the predictive model was 4.779 × 10-3, 1.967 × 10-3 and 1.574 × 10-3, respectively. CONCLUSION: The neural network method based on a dose-prediction model of OAR in brachytherapy using needle insertion was simple and reliable. In addition, it only addressed volumes of sub-organs to predict the dose of OAR, which we believe is worthy of further promotion and application.

Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.

Purpose: By using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model. Methods: CT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set. Results: Three radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85. Conclusion: The automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.

NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation.

BACKGROUND AND OBJECTIVES: Non-contrast CT (NCCT) and contrast-enhanced CT (CECT) are important diagnostic tools with distinct features and applications for chest diseases. We developed two synthesizers for the mutual synthesis of NCCT and CECT and evaluated their applications. METHODS: Two synthesizers (S1 and S2) were proposed based on a generative adversarial network. S1 generated synthetic CECT (SynCECT) from NCCT and S2 generated synthetic NCCT (SynNCCT) from CECT. A new training procedure for synthesizers was proposed. Initially, the synthesizers were pretrained using self-supervised learning (SSL) and dual-energy CT (DECT) and then fine-tuned using the registered NCCT and CECT images. Pulmonary vessel segmentation from NCCT was used as an example to demonstrate the effectiveness of the synthesizers. Two strategies (ST1 and ST2) were proposed for pulmonary vessel segmentation. In ST1, CECT images were used to train a segmentation model (Model-CECT), NCCT images were converted to SynCECT through S1, and SynCECT was input to Model-CECT for testing. In ST2, CECT data were converted to SynNCCT through S2. SynNCCT and CECT-based annotations were used to train an additional model (Model-NCCT), and NCCT was input to Model-NCCT for testing. Three datasets, D1 (40 paired CTs), D2 (14 NCCTs and 14 CECTs), and D3 (49 paired DECTs), were used to evaluate the synthesizers and strategies. RESULTS: For S1, the mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were 14.60± 2.19, 1644± 890, 34.34± 1.91, and 0.94± 0.02, respectively. For S2, they were 12.52± 2.59, 1460± 922, 35.08± 2.35, and 0.95± 0.02, respectively. Our synthesizers outperformed the counterparts of CycleGAN, Pix2Pix, and Pix2PixHD. The results of ablation studies on SSL pretraining, DECT pretraining, and fine-tuning showed that performance worsened (for example, for S1, MAE increased to 16.53± 3.10, 17.98± 3.10, and 20.57± 3.75, respectively). Model-NCCT and Model-CECT achieved dice similarity coefficients (DSC) of 0.77 and 0.86 on D1 and 0.77 and 0.72 on D2, respectively. CONCLUSIONS: The proposed synthesizers realized mutual and high-quality synthesis between NCCT and CECT images; the training procedures, including SSL pretraining, DECT pretraining, and fine-tuning, were critical to their effectiveness. The results demonstrated the usefulness of synthesizers for pulmonary vessel segmentation from NCCT images.

Effect of 3-Dimensional Interstitial High-Dose-Rate Brachytherapy With Regional Metastatic Lymph Node Intensity-Modulated Radiation Therapy in Locally Advanced Peripheral Non-Small Cell Lung Cancer: 5-Year Follow-up of a Phase 2 Clinical Trial.

PURPOSE: We aimed to reveal the 5-year clinical outcomes of 3-dimensional (3D) interstitial high-dose-rate (HDR) brachytherapy with regional metastatic lymph node intensity modulated radiation therapy (IMRT) for locally advanced peripheral non-small cell lung cancer (NSCLC), which has been shown to have low toxicity and improved 2-year survival rates in patients with this disease. METHODS AND MATERIALS: In this phase 2, single-arm, open-label clinical trial, 83 patients with locally advanced peripheral NSCLC were enrolled (median follow-up [range], 53.7 [4.3-120.4] months). All eligible patients received 3D interstitial HDR brachytherapy with regional metastatic lymph node IMRT. The primary endpoint was overall survival (OS). Secondary endpoints were local recurrence-free survival, regional recurrence-free survival, progression-free survival, distant metastasis-free survival, toxicities, and quality of life. RESULTS: The final analysis included 75 patients (19 [25.3%] females, 56 [74.7%] males; median [range] age, 64 [44-80] years; stage IIIA, 34 [45.3%]; stage IIIB, 41 [54.7%]). At the latest follow-up, 32 (42.7%) patients had survived. The median OS was 38.0 months (5-year OS, 44.5%; 95% confidence interval [CI], 33.8%-58.6%). Local recurrence-free survival, recurrence-free survival, and distant metastasis-free survival at 5 years were 79.2% (95% CI, 68.5%-91.5%), 73.6% (95% CI, 61.5%-88.1%), and 50.3% (95% CI, 38.3%-66.1%), respectively. The dominant failure pattern was distant disease, corresponding to 40% (30 of 75) of patients and 65.2% (30 of 46) of all failures. Two (2.7%) patients developed grade 1 acute pneumonitis. Grade 2 and 3 acute esophagitis occurred in 11 (14.7%) and 4 (5.3%) patients, respectively. No late radiation-related grade ≥2 late adverse events were observed. CONCLUSIONS: 3D interstitial HDR brachytherapy with regional metastatic lymph node IMRT for locally advanced peripheral NSCLC shows significant OS and has a low toxicity rate. Additional evaluation in a phase 3 trial is recommended to substantiate these findings.

Development and validation of radiomics machine learning model based on contrast-enhanced computed tomography to predict axillary lymph node metastasis in breast cancer.

Preoperative identification of axillary lymph node metastasis can play an important role in treatment selection strategy and prognosis evaluation. This study aimed to establish a clinical nomogram based on lymph node images to predict lymph node metastasis in breast cancer patients. A total of 193 patients with non-specific invasive breast cancer were divided into training (n = 135) and validation set (n = 58). Radiomics features were extracted from lymph node images instead of tumor region, and the least absolute shrinkage and selection operator logistic algorithm was used to select the extracted features and generate radiomics score. Then, the important clinical factors and radiomics score were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram, and the clinical benefit of using the nomogram was evaluated by decision curve analysis. We found that clinical N stage and radiomics score were independent clinical predictors. Besides, the nomogram accurately predicted axillary lymph node metastasis, yielding an area under the receiver operating characteristic curve of 0.95 (95% confidence interval 0.93-0.98) in the validation set, indicating satisfactory calibration. Decision curve analysis confirmed that the nomogram had higher clinical utility than clinical N stage or radiomics score alone. Overall, the nomogram based on radiomics features and clinical factors can help radiologists to predict axillary lymph node metastasis preoperatively and provide valuable information for individual treatment.

[68Ga]Ga-FAPI PET for the evaluation of digestive system tumors: systematic review and meta-analysis.

PURPOSE: Digestive system tumors are a group of tumors with high incidence in the world nowadays. The assessment of digestive system tumor metastasis by conventional imaging seems to be unsatisfactory. [68Ga]Ga-FAPI, which has emerged in recent years, seems to be able to evaluate digestive system tumor metastasis. We aimed to summarize the current evidence of [68Ga]Ga-FAPI PET/CT or PET/MR for the assessment of primary tumors, lymph node metastases, and distant metastases in digestive system tumors. Besides, we also aimed to perform a meta-analysis of the sensitivity of [68Ga]Ga-FAPI PET diagnosis to discriminate between digestive system tumors, primary lesions, and non-primary lesions (lymph node metastases and distant metastases). MATERIALS AND METHODS: PubMed, MEDLINE and Cochrane Library databases were searched from the beginning of the database build to August 12, 2022. All studies undergoing [68Ga]Ga-FAPI PET for the evaluation of digestive tumors were included in the screening and review. Screening and full text review was performed by 3 investigators and data extraction was performed by 2 investigators. Risk of bias was examined with the QUADAS-2 criteria. Diagnostic test meta-analysis was performed with a random-effects model. RESULTS: A total of 541 studies were retrieved. Finally, 22 studies were selected for the systematic review and 18 studies were included in the meta-analysis. In the 18 publications, a total of 524 patients with digestive system tumors, 459 primary tumor lesions of digestive system tumors, and 1921 metastatic lesions of digestive system tumors were included in the meta-analysis. Based on patients, the sensitivity of [68Ga]Ga-FAPI PET for the diagnosis of digestive system tumors was 0.98 (95% CI: 0.94-0.99). Based on lesions, the sensitivity of [68Ga]Ga-FAPI PET for the diagnostic evaluation of primary tumor lesions of the digestive system was 0.97 (95% CI: 0.93-0.99); the sensitivity of [68Ga]Ga-FAPI PET for the diagnostic evaluation of non-primary lesions (lymph node metastases and distant metastases) of the digestive system tumors was 0.94 (95% CI: 0.79-0.99). CONCLUSION: [68Ga]Ga-FAPI PET has high accuracy and its sensitivity is at a high level for the diagnostic evaluation of digestive system tumors. Clinicians, nuclear medicine physicians, and radiologists may consider using [68Ga]Ga-FAPI PET/CT or PET/MR in the evaluation of primary tumors, lymph node metastases, and distant metastases in digestive system tumors.

A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images.

BACKGROUND AND OBJECTIVE: Lobectomy is a curative treatment for localized lung cancer. The study aims to construct an automatic pipeline for segmenting pulmonary lobes before and after lobectomy from CT images. MATERIALS AND METHODS: Six datasets (D1 to D6) of 865 CT scans were collected from two hospitals and public resources. Four nnU-Net-based segmentation models were trained. A lobectomy classification was proposed to automatically recognize the category of the input CT images: before lobectomy or one of five types after lobectomy. Finally, the lobe segmentation before and after lobectomy was realized by integrating the four models and lobectomy classification. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average symmetric surface distance (ASSD) were used to evaluate the segmentations. RESULTS: The pre-operative model achieved an average DSC of 0.964, 0.929, 0.934, and 0.891 in the four datasets. In D1 and D2, the average HD95 was 4.18 and 7.74 mm and the average ASSD was 0.86 and 1.32 mm, respectively. The lobectomy classification achieved an accuracy of 100%. After lobectomy, an average DSC of 0.973 and 0.936, an average HD95 of 2.70 and 6.92 mm, an average ASSD of 0.57 and 1.78 mm were obtained in D1 and D2, respectively. The postoperative segmentation pipeline outperformed other counterparts and training strategies. CONCLUSIONS: The proposed pipeline can automatically segment pulmonary lobes before and after lobectomy from CT images and be applied to manage patients with lung cancer after lobectomy.

Development of a Radiotherapy Localisation Computed Tomography-Based Radiomic Model for Predicting Survival in Patients With Nasopharyngeal Carcinoma Treated With Intensity-Modulated Radiotherapy Following Induction Chemotherapy.

BACKGROUND: Our purpose is to develop a model combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics that can be used to estimate overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy. METHODS: We recruited 145 patients with pathologically confirmed nasopharyngeal carcinoma between February 2012 and April 2015. In total, 851 radiomic features were extracted from radiotherapy localisation computed tomography images for the gross tumour volume of the nasopharynx and the gross tumour volume of neck metastatic lymph nodes. The least absolute shrinkage and selection operator algorithm was applied to select radiomics features, build the model and calculate the Rad-score. The patients were divided into high- and low-risk groups based on their Rad-scores. A nomogram for estimating overall survival based on both radiomic and clinical features was generated using multivariate Cox regression hazard models. Prediction reliability was evaluated using Harrell's concordance index. RESULTS: In total, seven radiomic features and one clinical characteristic were extracted for survival analysis, and the combination of radiomic and clinical features was a better predictor of overall survival (concordance index = .849 [confidence interval: .782-.916]) than radiomic features (concordance index = .793 [confidence interval: .697-.890]) or clinical characteristics (concordance index = .661 [confidence interval: .673-.849]) alone. CONCLUSION: Our results show that a nomogram combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics can predict overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy more effectively than radiomic features or clinical characteristics alone.

Radiotherapy-induced enrichment of EGF-modified doxorubicin nanoparticles enhances the therapeutic outcome of lung cancer.

Chemotherapy is the primary treatment for advanced non-small-cell lung cancer (NSCLC). However, related dose-dependent toxicity limits its clinical use. Therefore, it is necessary to explore new strategies for improving the clinical outcomes while reducing the side effects of chemotherapy in the treatment of NSCLC. In this study, we designed and synthesized epidermal growth factor (EGF)-modified doxorubicin nanoparticles (EGF@DOX-NPs) that selectively targets the epidermal growth factor receptor (EGFR) overexpressed in lung tumor cells. When administered in combination with low-dose X-ray radiotherapy (RT), the NPs preferentially accumulated at the tumor site due to radiation-induced outburst of the local intra-tumoral blood vessels. Compared with DOX alone, EGF@DOX-NPs significantly decreased the viability and migration and enhanced the apoptosis rates of tumor cells in vitro. Also, the EGF@DOX-NPs significantly inhibited tumor growth in vivo, increasing the survival of the tumor-bearing mice without apparent systemic toxic effects through RT-induced aggregation. The tumor cell proliferation was greatly inhibited in the RT + EGF@DOX-NPs group. Contrarily, the apoptosis of tumor cells was significantly higher in this group. These results confirm the promising clinical application of radiotherapy in combination with EGF@DOX-NPs for lung cancer treatment.