Assessing the Impact of Charged Particle Radiation Therapy for Head and Neck Adenoid Cystic Carcinoma: A Systematic Review and Meta-Analysis.

Purpose: Head and neck adenoid cystic carcinoma (HNACC) is a radioresistant tumor. Particle therapy, primarily proton beam therapy and carbon-ion radiation, is a potential radiotherapy treatment for radioresistant malignancies. This study aims to conduct a meta-analysis to evaluate the impact of charged particle radiation therapy on HNACC. Methods: A comprehensive search was conducted in Pubmed, Cochrane Library, Web of Science, Embase, and Medline until December 31, 2022. The primary endpoints were overall survival (OS), local control (LC), and progression-free survival (PFS), while secondary outcomes included treatment-related toxicity. Version 17.0 of STATA was used for all analyses. Results: A total of 14 studies, involving 1297 patients, were included in the analysis. The pooled 5-year OS and PFS rates for primary HNACC were 78% (95% confidence interval [CI] = 66-91%) and 62% (95% CI = 47-77%), respectively. For all patients included, the pooled 2-year and 5-year OS, LC, and PFS rates were as follows: 86.1% (95% CI = 95-100%) and 77% (95% CI = 73-82%), 92% (95% CI = 84-100%) and 73% (95% CI = 61-85%), and 76% (95% CI = 68-84%) and 55% (95% CI = 48-62%), respectively. The rates of grade 3 and above acute toxicity were 22% (95% CI = 13-32%), while late toxicity rates were 8% (95% CI = 3-13%). Conclusions: Particle therapy has the potential to improve treatment outcomes and raise the quality of life for HNACC patients. However, further research and optimization are needed due to the limited availability and cost considerations associated with this treatment modality.

Efficacy and safety of particle therapy for inoperable stage II-III non-small cell lung cancer: a systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Particle therapy, mainly including carbon-ion radiotherapy (CIRT) and proton beam therapy (PBT), has dose distribution advantages compared to photon radiotherapy. It has been widely reported as a promising treatment method for early non-small cell lung cancer (NSCLC). However, its application in locally advanced non-small cell lung cancer (LA-NSCLC) is relatively rare, and its efficacy and safety are inconclusive. This study aimed to provide systematic evidence for evaluating the efficacy and safety of particle therapy for inoperable LA-NSCLC. METHODS: To retrieve published literature, a systematic search was conducted in PubMed, Web of Science, Embase, and Cochrane Library until September 4, 2022. The primary endpoints were local control (LC) rate, overall survival (OS) rate, and progression-free survival (PFS) rate at 2 and 5 years. The secondary endpoint was treatment-related toxicity. The pooled clinical outcomes and 95% confidence intervals (CIs) were calculated by using STATA 15.1. RESULTS: Nineteen eligible studies with a total sample size of 851 patients were included. The pooled data demonstrated that the OS, PFS, and LC rates at 2 years of LA-NSCLC treated by particle therapy were 61.3% (95% CI = 54.7-68.7%), 37.9% (95% CI = 33.8-42.6%) and 82.2% (95% CI = 78.7-85.9%), respectively. The pooled 5-year OS, PFS, and LC rates were 41.3% (95% CI = 27.1-63.1%), 25.3% (95% CI = 16.3-39.4%), and 61.5% (95% CI = 50.7-74.6%), respectively. Subgroup analysis stratified by treatment type showed that the concurrent chemoradiotherapy (CCRT, PBT combined with concurrent chemotherapy) group had better survival benefits than the PBT and CIRT groups. The incidence rates of grade 3/4 esophagitis, dermatitis, and pneumonia in LA-NSCLC patients after particle therapy were 2.6% (95% CI = 0.4-6.0%), 2.6% (95% CI = 0.5-5.7%) and 3.4% (95% CI = 1.4-6.0%), respectively. CONCLUSIONS: Particle therapy demonstrated promising efficacy and acceptable toxicity in LA-NSCLC patients.

Biological effects of cancer stem cells irradiated by charged particle: a systematic review of in vitro studies.

PURPOSE: The existence of cancer stem cells (CSCs) is closely related to tumor recurrence, metastasis, and resistance to chemoradiotherapy. In addition, given the unique physical and biological advantages of charged particle, we hypothesized that charged particle irradiation would produce strong killing effects on CSCs. The purpose of our systematic review is to evaluate the biological effects of CSCs irradiated by charged particle, including proliferation, invasion, migration, and changes in the molecular level. METHODS: We searched PubMed, EMBASE, and Web of Science until 17 march 2022 according to the key words. Included studies have to be vitro studies of CSCs irradiated by charged particle. Outcomes included one or more of radiation sensitivity, proliferation, metastasis, invasion, and molecular level changes, like DNA damage after been irradiated. RESULTS: Eighteen studies were included in the final analysis. The 18 articles include 12-carbon ion irradiation, 4-proton irradiation, 1 α-particle irradiation, 1-carbon ion combine proton irradiation. CONCLUSION: Through the extraction and analysis of data, we came to this conclusion: CSCs have obvious radio-resistance compared with non-CSCs, and charged particle irradiation or in combination with drugs could overcome this resistance, specifically manifested in inhibiting CSCs' proliferation, invasion, migration, and causing more and harder to repair DNA double-stranded breaks (DSB) of CSCs.

Clinical outcomes of conventional HDR intracavitary brachytherapy combined with complementary applicator-guided intensity modulated radiotherapy boost in patients with bulky cervical tumour.

OBJECTIVE: To investigate the clinical outcomes and toxicity in patients with locally advanced cervical cancer treated with supplementary applicator guided-intensity modulated radiation therapy (IMRT) based on conventional intracavitary brachytherapy (IC/IMRT). DESIGN: A retrospective cohort study. SETTING: Sichuan Cancer Hospital & Institute, Sichuan Cancer Centre, China. POPULATION: Large high-risk clinical target volume (HR-CTV) volume (>40 ml) at the time of brachytherapy cervical cancer patients were recruited. METHODS: This study is a retrospective analysis of 76 patients with locally advanced cervical cancer (FIGO IIB-IVA) treated with concurrent chemoradiotherapy followed by IC/IMRT between June 2010 and October 2016. External radiotherapy (45 Gy in 25 fractions) was adminstered with cisplatin chemotherapy treatment before IC/IMRT. The IMRT plan was optimised using the ICBT plan base dose plan by an inverse dose optimisation tool which allows the use of DVH constraints on the total dose of ICBT. A seven-field gantry angle IMRT plan was devised to avoid hotspots when optimising the boost plan. The prescription dose for HR-CTV and IR-CTV were 6 and 5 Gy per fraction for five fractions, respectively. RESULTS: Mean HR-CTV was 65.8 ± 23.6 ml at the time of brachytherapy. D90 for HR-CTV and IR-CTV were 88.7 ± 3.6 Gy and 78.1 ± 2.5 Gy. D2cc for bladder, rectum, sigmoid and small intestine were 71.8 ± 3.8, 64.6 ± 4.9, 63.9 ± 5.3 and 56.7 ± 8.7 Gy, respectively. Median follow-up was 85 months (47.9-124.2 months). Five-year local recurrence-free survival rate, metastasis recurrence-free survival rate, disease-free survival rate and cancer-special survival rate were 87.6, 82.4, 70.9 and 76.3%, respectively. The grade 1 + 2 gastrointestinal and urinary late toxicities were 15.8 and 21.1%, and grade 3 late toxicities were 3.9 and 5.2%, respectively. Neither acute nor late grade 4 gastrointestinal or urinary toxicities were seen. CONCLUSIONS: The combination of ICBT with an applicator-guided supplementary IMRT boost achieved excellent local control and overall survival with low toxicity for bulky residual cervical tumour.

Does particle radiation have superior radiobiological advantages for prostate cancer cells? A systematic review of in vitro studies.

BACKGROUND: Charged particle beams from protons to carbon ions provide many significant physical benefits in radiation therapy. However, preclinical studies of charged particle therapy for prostate cancer are extremely limited. The aim of this study was to comprehensively investigate the biological effects of charged particles on prostate cancer from the perspective of in vitro studies. METHODS: We conducted a systematic review by searching EMBASE (OVID), Medline (OVID), and Web of Science databases to identify the publications assessing the radiobiological effects of charged particle irradiation on prostate cancer cells. The data of relative biological effectiveness (RBE), surviving fraction (SF), standard enhancement ratio (SER) and oxygen enhancement ratio (OER) were extracted. RESULTS: We found 12 studies met the eligible criteria. The relative biological effectiveness values of proton and carbon ion irradiation ranged from 0.94 to 1.52, and 1.67 to 3.7, respectively. Surviving fraction of 2 Gy were 0.17 ± 0.12, 0.55 ± 0.20 and 0.53 ± 0.16 in carbon ion, proton, and photon irradiation, respectively. PNKP inhibitor and gold nanoparticles were favorable sensitizing agents, while it was presented poorer performance in GANT61. The oxygen enhancement ratio values of photon and carbon ion irradiation were 2.32 ± 0.04, and 1.77 ± 0.13, respectively. Charged particle irradiation induced more G0-/G1- or G2-/M-phase arrest, more expression of γ-H2AX, more apoptosis, and lower motility and/or migration ability than photon irradiation. CONCLUSIONS: Both carbon ion and proton irradiation have advantages over photon irradiation in radiobiological effects on prostate cancer cell lines. Carbon ion irradiation seems to have further advantages over proton irradiation.

The Prognostic Value of Tumor Size, Volume and Tumor Volume Reduction Rate During Concurrent Chemoradiotherapy in Patients With Cervical Cancer.

Objective: This study aimed to investigate the relationship between prognostic and tumor parameters of cervical cancer patients, such as tumor size (TS), tumor volume (TV), and tumor volume reduction rate (TVRR) after external beam radiotherapy. Methods: A total of 217 patients with advanced cervical cancer, classified as Federation of Gynecology and Obstetrics (FIGO) IIa-IVa, were enrolled in the study. Pre- and mid-RT pelvic magnetic resonance imaging (MRI) were performed twice, during RT and just before brachytherapy. Results: The median follow-up time was 51 months (range, 7-111 months). The 5-year overall survival (OS), progression-free survival (PFS), and local failure-free survival (LFFS) rates were 81.3, 85.1, and 92.9%, respectively. Multivariate analysis revealed that tumor parameters including FIGO stage >II (Hazard Ratio, 2.377 and 95% confidence interval [CI], 1.091-5.182; P = 0.029), pre-RT TV >61.6 cm3 (HR, 0.417 and 95% CI, 0.188-0.926; P = 0.032), and mid-RT TV >11.38 cm3 (HR, 3.192 and 95% CI, 1.094-9.316; P = 0.034) were observably associated with OS. Univariate analysis showed that the tumor volume reduction rate (TVRR) was dramatically associated with overall survival (HR, 0.204 and 95% CI 0.033-1.282; P <0.001) and local failure-free survival (P = 0.050). Conclusions: In this retrospective study, TVRR and mid-radiotherapy tumor volume are independent and strong prognostic parameters for patients with local advanced cervical cancer receiving CCRT.

Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer.

OBJECTIVES: EGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy. MATERIALS AND METHODS: We retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of EGFR gene testing. Then manually segmented VOI, captured the clinicopathological features, analyzed traditional radiology features, and extracted radiomic, and deep learning features. The cases were randomly divided into training and test set. We carried out feature screening; then applied the light GBM algorithm, Resnet-101 algorithm, logistic regression to develop sole models, and fused models to predict EGFR mutation conditions. The efficiency of models was evaluated by ROC and PRC curves. RESULTS: We successfully established Modelclinical, Modelradiomic, ModelCNN (based on clinical-radiology, radiomic and deep learning features respectively), Modelradiomic+clinical (combining clinical-radiology and radiomic features), and ModelCNN+radiomic+clinical (combining clinical-radiology, radiomic, and deep learning features). Among the prediction models, ModelCNN+radiomic+clinical showed the highest performance, followed by ModelCNN, and then Modelradiomic+clinical. All three models were able to accurately predict EGFR mutation with AUC values of 0.751, 0.738, and 0.684, respectively. There was no significant difference in the AUC values between ModelCNN+radiomic+clinical and ModelCNN. Further analysis showed that ModelCNN+radiomic+clinical effectively improved the efficacy of Modelradiomic+clinical and showed better efficacy than ModelCNN. The inclusion of clinical-radiology features did not effectively improve the efficacy of Modelradiomic. CONCLUSIONS: Either deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.

OBJECTIVES: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma. METHODS: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis. RESULTS: Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set. CONCLUSIONS: The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules.

Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815-0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712-0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC.

PURPOSE: This study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC). METHODS: This study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis. RESULTS: The radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736-0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642-0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer-Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort). CONCLUSION: Radiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

BACKGROUND: Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans. METHODS: A total of 7,473 annotated traumatic rib fractures from 900 patients in a single center were enrolled into our dataset, named RibFrac Dataset, which were annotated with a human-in-the-loop labeling procedure. We developed a deep learning model, named FracNet, to detect and segment rib fractures. 720, 60 and 120 patients were randomly split as training cohort, tuning cohort and test cohort, respectively. Free-Response ROC (FROC) analysis was used to evaluate the sensitivity and false positives of the detection performance, and Intersection-over-Union (IoU) and Dice Coefficient (Dice) were used to evaluate the segmentation performance of predicted rib fractures. Observer studies, including independent human-only study and human-collaboration study, were used to benchmark the FracNet with human performance and evaluate its clinical applicability. A annotated subset of RibFrac Dataset, including 420 for training, 60 for tuning and 120 for test, as well as our code for model training and evaluation, was open to research community to facilitate both clinical and engineering research. FINDINGS: Our method achieved a detection sensitivity of 92.9% with 5.27 false positives per scan and a segmentation Dice of 71.5%on the test cohort. Human experts achieved much lower false positives per scan, while underperforming the deep neural networks in terms of detection sensitivities with longer time in diagnosis. With human-computer collobration, human experts achieved higher detection sensitivities than human-only or computer-only diagnosis. INTERPRETATION: The proposed FracNet provided increasing detection sensitivity of rib fractures with significantly decreased clinical time consumed, which established a clinically applicable method to assist the radiologist in clinical practice. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section. The funding sources played no role in the study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article for publication .

Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.

OBJECTIVES: To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). METHODS: This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. RESULTS: Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. CONCLUSIONS: The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. KEY POINTS: • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma.

Purpose: Up to 50% of Asian patients with NSCLC have EGFR gene mutations, indicating that selecting eligible patients for EGFR-TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict EGFR mutation status and subtypes. Materials and Methods: We included 637 patients with lung adenocarcinomas, who performed the EGFR mutations analysis in the current study. The whole dataset was randomly split into a training dataset (n = 322) and validation dataset (n = 315). A sub-dataset of EGFR-mutant lesions (EGFR mutation in exon 19 and in exon 21) was used to explore the capability of radiomic features for predicting EGFR mutation subtypes. Four hundred seventy-five radiomic features were extracted and a radiomics sore (R-score) was constructed by using the least absolute shrinkage and selection operator (LASSO) regression in the training dataset. A radiomics-based nomogram, incorporating clinical characteristics, CT features and R-score was developed in the training dataset and evaluated in the validation dataset. Results: The constructed R-scores achieved promising performance on predicting EGFR mutation status and subtypes, with AUCs of 0.694 and 0.708 in two validation datasets, respectively. Moreover, the constructed radiomics-based nomograms excelled the R-scores, clinical, CT features alone in terms of predicting EGFR mutation status and subtypes, with AUCs of 0.734 and 0.757 in two validation datasets, respectively. Conclusions: Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status and thus potentially fulfill the ultimate purpose of precision medicine. The methodology is a possible promising strategy to predict EGFR mutation subtypes, providing the support of clinical treatment scenario.