MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images. METHODS: In this retrospective study, T1-weighted (T1) and T1-weighted contrast-enhanced (T1ce) MRI images of 649 patients who underwent radiotherapy from August 2019 to January 2022 were included. A total of 5163 metastases were manually annotated by neuroradiologists. A two-stage deep learning model was developed for automatic detection and segmentation of BMs, which consisted of a lightweight segmentation network for generating metastases proposals and a multi-scale classification network for false-positive suppression. Its performance was evaluated by sensitivity, precision, F1-score, dice, and relative volume difference (RVD). RESULTS: Six hundred forty-nine patients were randomly divided into training (n = 295), validation (n = 99), and testing (n = 255) sets. The proposed two-stage model achieved a sensitivity of 90% (1463/1632) and a precision of 56% (1463/2629) on the testing set, outperforming one-stage methods based on a single-shot detector, 3D U-Net, and nnU-Net, whose sensitivities were 78% (1276/1632), 79% (1290/1632), and 87% (1426/1632), and the precisions were 40% (1276/3222), 51% (1290/2507), and 53% (1426/2688), respectively. Particularly for BMs smaller than 5 mm, the proposed model achieved a sensitivity of 66% (116/177), far superior to one-stage models (21% (37/177), 36% (64/177), and 53% (93/177)). Furthermore, it also achieved high segmentation performance with an average dice of 81% and an average RVD of 20%. CONCLUSION: A two-stage deep learning model can detect and segment BMs with high sensitivity and low volume error. KEY POINTS: • A two-stage deep learning model based on triple-channel MRI images identified brain metastases with 90% sensitivity and 56% precision. • For brain metastases smaller than 5 mm, the proposed two-stage model achieved 66% sensitivity and 22% precision. • For segmentation of brain metastases, the proposed two-stage model achieved a dice of 81% and a relative volume difference (RVD) of 20%.

The therapeutic utility of combining dynamic contrast-enhanced magnetic resonance imaging with arterial spin labeling in the staging of nasopharyngeal carcinoma.

BACKGROUND: To research the pathological and clinical staging uses of arterial spin labeling (ASL) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: 64 newly diagnosed nasopharyngeal carcinoma (NPC) patients were enrolled from December 2020 to January 2022, and 3.0 T MRI (Discovery 750W, GE Healthcare, USA) were used for ASL and DCE-MRI scans. The DCE-MRI and ASL raw data were processed post-acquisition on the GE image processing workstation (GE Healthcare, ADW 4.7, USA). The volume transfer constant (Ktrans), blood flow (BF), and accompanying pseudo-color images were generated automatically. Draw the region of interest (ROIs), and the Ktrans and BF values for each ROI were recorded separately. Based on pathological information and the most recent AJCC staging criteria, patients were divided into low T stage groups = T1-2 and high T stage groups = T3-4, low N stage groups = N0-1 and high N stage groups = N2-3, and low AJCC stage group = stage I-II and high AJCC stage group = stage III-IV. The association between the Ktranst and BF parameters and the T, N, and AJCC stages was compared using an independent sample t-test. Using a receiver operating characteristic (ROC) curve, the sensitivity, specificity, and AUC of Ktranst, BFt, and their combined use in T and AJCC staging of NPC were investigated and assessed. RESULT: The tumor-BF (BFt) (t = - 4.905, P < 0.001) and tumor-Ktrans (Ktranst) (t = - 3.113, P = 0.003) in the high T stage group were significantly higher than those in the low T stage group. The Ktranst in the high N stage group was significantly higher than that in the low N stage group (t = - 2.071, P = 0.042). The BFt (t = - 3.949, P < 0.001) and Ktranst (t = - 4.467, P < 0.001) in the high AJCC stage group were significantly higher than those in the low AJCC stage group. BFt was moderately positively correlated with the T stage (r = 0.529, P < 0.001) and AJCC stage (r = 0.445, P < 0.001). Ktranst was moderately positively correlated with T staging (r = 0.368), N staging (r = 0.254), and AJCC staging (r = 0.411). There was also a positive correlation between BF and Ktrans in gross tumor volume (GTV) (r = 0.540, P < 0.001), parotid (r = 0.323, P < 0.009) and lateral pterygoid muscle (r = 0.445, P < 0.001). The sensitivity of the combined application of Ktranst and BFt for AJCC staging increased from 76.5 and 78.4 to 86.3%, and the AUC value increased from 0.795 and 0.819 to 0.843, respectively. CONCLUSION: Combining Ktrans and BF measures may make it possible to identify the clinical stages in NPC patients.

An Effective Approach to Improve the Automatic Segmentation and Classification Accuracy of Brain Metastasis by Combining Multi-phase Delay Enhanced MR Images.

The objective of this study is to analyse the diffusion rule of the contrast media in multi-phase delayed enhanced magnetic resonance (MR) T1 images using radiomics and to construct an automatic classification and segmentation model of brain metastases (BM) based on support vector machine (SVM) and Dpn-UNet. A total of 189 BM patients with 1047 metastases were enrolled. Contrast-enhanced MR images were obtained at 1, 3, 5, 10, 18, and 20 min following contrast medium injection. The tumour target volume was delineated, and the radiomics features were extracted and analysed. BM segmentation and classification models in the MR images with different enhancement phases were constructed using Dpn-UNet and SVM, and differences in the BM segmentation and classification models with different enhancement times were compared. (1) The signal intensity for BM decreased with time delay and peaked at 3 min. (2) Among the 144 optimal radiomics features, 22 showed strong correlation with time (highest R-value = 0.82), while 41 showed strong correlation with volume (highest R-value = 0.99). (3) The average dice similarity coefficients of both the training and test sets were the highest at 10 min for the automatic segmentation of BM, reaching 0.92 and 0.82, respectively. (4) The areas under the curve (AUCs) for the classification of BM pathology type applying single-phase MRI was the highest at 10 min, reaching 0.674. The AUC for the classification of BM by applying the six-phase image combination was the highest, reaching 0.9596, and improved by 42.3% compared with that by applying single-phase images at 10 min. The dynamic changes of contrast media diffusion in BM can be reflected by multi-phase delayed enhancement based on radiomics, which can more objectively reflect the pathological types and significantly improve the accuracy of BM segmentation and classification.

Functional MRI radiomics-based assessment of pelvic bone marrow changes after concurrent chemoradiotherapy for cervical cancer.

OBJECTIVES: To quantify the dose-response relationship of changes in pelvic bone marrow (PBM) functional MR radiomic features (RF) during concurrent chemoradiotherapy (CCRT) for patients with cervical cancer and establish the correlation with hematologic toxicity to provide a basis for PBM sparing. METHODS: A total of 54 cervical cancer patients who received CCRT were studied retrospectively. Patients underwent MRI IDEAL IQ and T2 fat suppression (T2fs) scanning pre- and post-CCRT. The PBM RFs were extracted from each region of interest at dose gradients of 5-10 Gy, 10-15 Gy, 15-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy, and > 50 Gy, and changes in peripheral blood cell (PBC) counts during radiotherapy were assessed. The dose-response relationship of RF changes and their correlation with PBC changes were investigated. RESULTS: White blood cell, neutrophils (ANC) and lymphocyte counts during treatment were decreased by 49.4%, 41.4%, and 76.3%, respectively. Most firstorder features exhibited a significant dose-response relationship, particularly FatFrac IDEAL IQ, which had a maximum dose-response curve slope of 10.09, and WATER IDEAL IQ had a slope of - 7.93. The firstorder-Range in FAT IDEAL IQ and firstorder-10Percentile in T2fs, showed a significant correlation between the changes in ANC counts under the low dose gradient of 5-10 Gy (r = 0.744, -0.654, respectively, p < 0.05). CONCLUSION: Functional MR radiomics can detect microscopic changes in PBM at various dose gradients and provide an objective reference for bone marrow sparing and dose limitation in cervical cancer CCRT.

Quantitative study of the changes in brain white matter before and after radiotherapy by applying multi-sequence MR radiomics.

PURPOSE: To analyse the changes in brain white matter before and after radiotherapy (RT) by applying multisequence MR radiomics features and to establish a relationship between the changes in radiomics features and radiation dose. METHODS: Eighty-eight patients with brain tumours who had undergone RT were selected in this study, and MR images (T1, T1+C, T2FLAIR, T2, DWI, and ASL) before and after RT were obtained. The brain white matter was delineated as an ROI under dose gradients of 0-5 Gy, 5-10 Gy, 10-15 Gy, 15-20 Gy, 20-30 Gy, 30-40 Gy, and 40-50 Gy. The radiomics features of each ROI were extracted, and the changes in radiomics features before and after RT for different sequences under different dose gradients were compared. RESULTS: At each dose gradient, statistically significant features of different MR sequences were mainly concentrated in three dose gradients, 5-10 Gy, 20-30 Gy, and 30-40 Gy. The T1+C sequence held the most features (66) under the 20-30 Gy dose gradient. There were 20 general features at dose gradients of 20-30 Gy, 30-40 Gy, and 40-50 Gy, and the changes in features first decreased and then increased following dose escalation. With dose gradients of 5-10 Gy and 10-15 Gy, only T1 and T2FLAIR had general features, and the rates of change were - 24.57% and - 29.32% for T1 and - 3.08% and - 10.87% for T2FLAIR, respectively. The changes showed an upward trend with increasing doses. For different MR sequences that were analysed under the same dose gradient, all sequences with 5-10 Gy, 20-30 Gy and 30-40 Gy had general features, except the T2FLAIR sequence, which was concentrated in the FirstOrder category feature, and the changes in features of T1 and T1+C were more significant than those of the other sequences. CONCLUSIONS: MR radiomics features revealed microscopic changes in brain white matter before and after RT, although there was no constant dose-effect relationship for each feature. The changes in radiomics features in different sequences could reveal the radiation response of brain white matter to different doses.

COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment.

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.

A preliminary investigation of re-evaluating the irradiation dose in hepatocellular carcinoma radiotherapy applying 4D CT and deformable registration.

PURPOSE: To investigate the effect of breathing motion on dose distribution for hepatocellular carcinoma (HCC) patients using four-dimensional (4D) CT and deformable registration. METHODS: Fifty HCC patients who were going to receive radiotherapy were enrolled in this study. All patients had been treated with transarterial chemoembolization beforehand. Three-dimensional (3D) and 4D CT scans in free breathing were acquired sequentially. Volumetric modulated arc therapy (VMAT) was planned on the 3D CT images and maximum intensity projection (MIP) images. Thus, the 3D dose (Dose-3D ) and MIP dose (Dose-MIP ) were obtained, respectively. Then, the Dose-3D and Dose-MIP were recalculated on 10 phases of 4D CT images, respectively, in which the end-inhale and end-exhale phase doses were defined as Dose-3D-EI , Dose-3D-EE , Dose-MIP-EI , and Dose-MIP-EE . The 4D dose (Dose-4D-3D and Dose-4D-MIP ) were obtained by deforming 10 phase doses to the end-exhale CT to accumulate. The dosimetric difference in Dose-3D , Dose-EI3D , Dose-EE3D , Dose-4D-3D , Dose-MIP , Dose-EIMIP , Dose-EEMIP , and Dose-4D-MIP were compared to evaluate the motion effect on dose delivery to the planning target volume (PTV) and normal liver. RESULTS: Compared with Dose-3D , PTV D99 in Dose-EI3D , Dose-EE3D and Dose-4D-3D decreased by an average of 6.02%, 1.32%, 2.43%, respectively (P < 0.05); while PTV D95 decreased by an average of 3.34%, 1.51%, 1.93%, respectively (P < 0.05). However, CI and HI of the PTV in Dose-3D was superior to the other three distributions (P < 0.05). There was no significant differences for the PTV between Dose-EI and Dose-EE , and between the two extreme phase doses and Dose-4D (P> 0.05). Negligible difference was observed for normal liver in all dose distributions (P> 0.05). CONCLUSIONS: Four-dimensional dose calculations potentially ensure target volume coverage when breathing motion may affect the dose distribution. Dose escalation can be considered to improve the local control of HCC on the basis of accurately predicting the probability of radiation-induced liver disease.

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.

BACKGROUND: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). METHODS: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. CONCLUSIONS: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Thalamocortical neural responses during hyperthermia: a resting-state functional MRI study.

The neural responses during hyperthermia, once thought of as simple physiological processes (e.g. thermal sensation and regulation), have now been recognised involving more cognitive processes, which would be of high importance to the management of those occupations during heavy heat exposure. Previous studies have demonstrated altered activity in localised subcortical clusters for thermal sensation and regulation, as well as cortical-cortical activity for behavioural tasks during hyperthermia. However, the involvement of cortical-subcortical activity during hyperthermia has not been investigated. In this study, we performed exploratory analyses comparing thalamocortical functional connectivity during whole body hyperthermic condition for an hour at 50 °C and normothermic condition at 25 °C. We found weakened functional connectivity of cortical fronto-polar/anterior cingulate cortex and prefrontal areas with the corresponding thalamic nuclei during hyperthermic versus normothermic comparisons. On the contrary, the motor/premotor, somatosensory and temporal cortical subdivisions showed increased connectivity with thalamic nuclei during hyperthermia. Thalamocortical connectivity changes in the prefrontal were identified to be correlated with the behavioural reaction time during psychomotor vigilance test after controlling for physiological variables. These distinct thalamocortical pathway alterations might reflect physiologically thermal sensation and regulation, as well as psychologically neural behaviour changes underlying cortical-subcortical activity during hyperthermia.

Predictive value of blood lipid association with response to neoadjuvant chemoradiotherapy in colorectal cancer.

The aim of this research was to explore whether blood lipid parameters could predict tumor regression grading (TRG) and compare with the predictive value of carcinoembryonic antigen (CEA) in patients with locally advanced colorectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Between June 2011 and January 2015, the records of 176 patients with primary colorectal adenocarcinoma treated with nCRT followed by radical surgery were reviewed retrospectively. Total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and pre-CEA were measured before nCRT, and post-CEA was measured before surgery. A total of 129 (73.3 %) good responders (TRG 3-4) and 47 (26.7 %) poor responders (TRG 0-2) were assessed after the nCRT. TC, LDL, HDL, and ΔCEA were 6.56 ± 0.95, 3.08 ± 0.72, and 1.43 ± 0.25 mmol/L and -0.69 ± 8.33 µg/mL in poor responders compared with 5.15 ± 1.29, 2.39 ± 0.5, and 1.37 ± 0.32 mmol/L and 16.67 ± 30.18 µg/mL in good responders, respectively (p < 0.05). TG, pre-CEA, and post-CEA were not significantly different. Multivariate logistic regression analysis revealed TC and ΔCEA as independent factors in predicting TRG; TC showed a sensitivity of 62.79 %, a specificity of 91.49 %, a Youden index of 0.543, a cutoff value of 5.52, and an AUC of 0.800 compared with ΔCEA (sensitivity 76.74 %, specificity 65.96 %, Youden index 0.427, and AUC 0.761). TC has a better predictive value than ΔCEA and hence might serve as a predictor of TRG in LARC patients.

The error estimate for contouring the brainstem in radiotherapy of head and neck cancer: a multi-center study from north China.

PURPOSE: To analyze the error in contouring the brainstem for patients with head and neck cancer who underwent radiotherapy (RT) based on computed tomography (CT) and magnetic resonance (MR) images. METHODS: 20 patients with brain tumor and 17 patients with nasopharyngeal cancer (NPC) were randomly selected. Each patient underwent MR and CT scanning. For each patient, one observer contoured the brainstem on CT and MR images 10 times, and 10 observers from 5 centers delineated the brainstem on CT and MR images only one time. The inter- and intra-observers volume and outline variations were compared. RESULTS: The volumes of brainstem contoured by inter and intra-observers on CT and MR images were similar (p>0.05). The reproducibility of contouring brainstem on MR images was better than that on CT images (p<0.05) for both inter- and intra-observer variability. The inter- and intra-observer variability for contouring the brainstem on CT images reached mean values of 0.81±0.05 (p>0.05) and of 0.85±0.05 (p>0.05), respectively, while on MR images the respective values were 0.90±0.05 (p>0.05) and 0.92±0.04 (p>0.05). CONCLUSION: Contouring the brainstem on MR images was more accurate and reproducible than that on CT images. Precise information might be more helpful for the patients whose lesion were closed to brainstem.

Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.

Background: The primary objective of this research is to devise a model to predict the pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT). Methods: We retrospectively analyzed data from 60 ESCC patients who received nICRT between 2019 and 2023. These patients were divided into two cohorts: pCR-group (N = 28) and non-pCR group (N = 32). Radiomic features, discerned from the primary tumor region across plain, arterial, and venous phases of CT, and pertinent laboratory data were documented at two intervals: pre-treatment and preoperation. Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. The model's discriminating capability was evaluated using the area under the receiver operating characteristic curve (AUC). Additionally, the clinical applicability of the clinical-radiomic model was appraised through decision curve analysis (DCA). Results: The clinical-radiomic model incorporated seven significant markers: postHALP, ΔHB, post-ALB, firstorder_Skewness, GLCM_DifferenceAverage, GLCM_JointEntropy, GLDM_DependenceEntropy, and NGTDM_Complexity, to predict pCR. The XGBoost algorithm rendered an accuracy of 0.87 and an AUC of 0.84. Notably, the joint omics approach superseded the performance of solely radiomic or clinical model. The DCA further cemented the robust clinical utility of our clinical-radiomic model. Conclusion: This study successfully formulated and validated a union omics methodology for anticipating the therapeutic outcomes of nICRT followed by radical surgical resection. Such insights are invaluable for clinicians in identifying potential nICRT responders among ESCC patients and tailoring optimal individualized treatment plans.

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma.

Hepatocellular Carcinoma (HCC) is one of the most common malignant neoplasms. With the advancement of technology, the precision of radiotherapy (RT) for HCC has considerably increased, and it is an indispensable modality in the comprehensive management of HCC. Some RT techniques increase the radiation dose to HCC, which decreases the radiation dose delivered to the surrounding normal liver tissue. This approach significantly improves the efficacy of HCC treatment and reduces the incidence of Radiation-induced Liver Disease (RILD). Clear imaging and precise determination of the Gross Target Volume (GTV) are prerequisites of precise RT of HCC. The main hindrances in determining the HCC GTV include indistinct tumor boundaries on imaging and the impact on respiratory motion. The integration of multimodal imaging, four-dimensional imaging, and artificial intelligence (AI) techniques can help overcome challenges for HCC GTV. In this article, the advancements in medical imaging and precise determination for HCC GTV have been reviewed, providing a framework for the precise RT of HCC.

Advances in determining the gross tumor target volume for radiotherapy of brain metastases.

Brain metastases (BMs) are the most prevalent intracranial malignant tumors in adults and are the leading cause of mortality attributed to malignant brain diseases. Radiotherapy (RT) plays a critical role in the treatment of BMs, with local RT techniques such as stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) showing remarkable therapeutic effectiveness. The precise determination of gross tumor target volume (GTV) is crucial for ensuring the effectiveness of SRS/SBRT. Multimodal imaging techniques such as CT, MRI, and PET are extensively used for the diagnosis of BMs and GTV determination. With the development of functional imaging and artificial intelligence (AI) technology, there are more innovative ways to determine GTV for BMs, which significantly improve the accuracy and efficiency of the determination. This article provides an overview of the progress in GTV determination for RT in BMs.

The effect of time-delayed contrast-enhanced scanning in determining the gross tumor target volume of large-volume brain metastases.

BACKGROUND AND PURPOSE: To assess the variation of large-volume brain metastases (BMs) boundaries and shapes using enhanced magnetic resonance (MR) scanning with different delay times and to provide a basis for determining the gross tumor target volume (GTV) for radiotherapy of BMs. MATERIALS AND METHODS: We prospectively enrolled 155 patients initially diagnosed with BMs (561 lesions > 1 cm). Contrast-enhanced (CE) T1-weighted imaging scans were performed 1, 3, 5, 10, 18, and 20 min after gadolinium-based contrast agent injection and GTVs were determined as GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min, respectively, which were subsequently fused in different phases. Fusion of the six GTVs was defined as GTV-total, which was set as the reference GTV. The volume, shape, and signal intensity of the GTVs and brain white matter (BWM) were compared at different delay times. RESULTS: GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes increased by 2.2 %, 3.8 %, 6.5 %, 9.5 %, and 10.6 %, respectively (P < 0.05) compared with GTV-1min. Compared with GTV-total, GTV-1min, GTV-3min, GTV-5min, GTV-10min, GTV-18min, and GTV-20min volumes reduced by 25.4 %, 22.1 %, 18.7 %, 15.0 %, 11.2 %, and 10.3 %, respectively (P < 0.05). Compared with GTV-total, 29 (51.8 %) fused GTVs had a volume reduction rate < 5 %, 45 (80.4 %) had a Dice similarity coefficient > 0.95, and all contained GTV-10min, GTV-18min or GTV-20min. The signal intensity ratio between the GTV and BWM peaked at 5 min (0.351 ± 0.24). CONCLUSION: Enhanced MR scans with different delay times show significant differences in the boundaries and shapes of large-volume BMs, and time-delayed multi-phase CE scanning should be used in GTV determination, with time phases ≥ 10 min being mandatory.

Hippocampal sparing in whole-brain radiotherapy for brain metastases: controversy, technology and the future.

Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain metastases (BMs), but cognitive decline after WBRT seriously affects patients' quality of life. The development of cognitive dysfunction is closely related to hippocampal injury, but standardized criteria for predicting hippocampal injury and dose limits for hippocampal protection have not yet been developed. This review systematically reviews the clinical efficacy of hippocampal avoidance - WBRT (HA-WBRT), the controversy over dose limits, common methods and characteristics of hippocampal imaging and segmentation, differences in hippocampal protection by common radiotherapy (RT) techniques, and the application of artificial intelligence (AI) and radiomic techniques for hippocampal protection. In the future, the application of new techniques and methods can improve the consistency of hippocampal dose limit determination and the prediction of the occurrence of cognitive dysfunction in WBRT patients, avoiding the occurrence of cognitive dysfunction in patients and thus benefiting more patients with BMs.

Quantitative analysis of the impact of respiratory state on the heartbeat-induced movements of the heart and its substructures.

PURPOSE: This study seeks to examine the influence of the heartbeat on the position, volume, and shape of the heart and its substructures during various breathing states. The findings of this study will serve as a valuable reference for dose-volume evaluation of the heart and its substructures in radiotherapy for treating thoracic tumors. METHODS: Twenty-three healthy volunteers were enrolled in this study, and cine four-dimensional magnetic resonance images were acquired during periods of end-inspiration breath holding (EIBH), end-expiration breath holding (EEBH), and deep end-inspiration breath holding (DIBH). The MR images were used to delineate the heart and its substructures, including the heart, pericardium, left ventricle (LV), left ventricular myocardium, right ventricle (RV), right ventricular myocardium (RVM), ventricular septum (VS), atrial septum (AS), proximal and middle portions of the left anterior descending branch (pmLAD), and proximal portion of the left circumflex coronary branch (pLCX). The changes in each structure with heartbeat were compared among different respiratory states. RESULTS: Compared with EIBH, EEBH increased the volume of the heart and its substructures by 0.25-3.66%, while the average Dice similarity coefficient (DSC) increased by - 0.25 to 8.7%; however, the differences were not statistically significant. Conversely, the VS decreased by 0.89 mm in the left-right (LR) direction, and the displacement of the RV in the anterior-posterior (AP) direction significantly decreased by 0.76 mm (p < 0.05). Compared with EIBH and EEBH, the average volume of the heart and its substructures decreased by 3.08-17.57% and 4.09-20.43%, respectively, during DIBH. Accordingly, statistically significant differences (p < 0.05) were observed in the volume of the heart, pericardium, LV, RV, RVM, and AS. The average DSC increased by 0-37.04% and - 2.6 to 32.14%, respectively, with statistically significant differences (p < 0.05) found in the right ventricular myocardium and interatrial septum. Furthermore, the displacements under DIBH decreased in the three directions (i.e.,- 1.73 to 3.47 mm and - 0.36 to 2.51 mm). In this regard, the AP displacement of the heart, LV, RV, RVM, LR direction, LV, RV, and AS showed statistically significant differences (p < 0.05). The Hausdorff distance (HD) of the heart and its substructures under the three breathing states are all greater than 11 mm. CONCLUSION: The variations in the displacement and shape alterations of the heart and its substructures during cardiac motion under various respiratory states are significant. When assessing the dose-volume index of the heart and its substructures during radiotherapy for thoracic tumors, it is essential to account for the combined impacts of cardiac motion and respiration.

Evaluation of specific RBE in different cells of hippocampus under high-dose proton irradiation in rats.

The study aimed to determine the specific relative biological effectiveness (RBE) of various cells in the hippocampus following proton irradiation. Sixty Sprague-Dawley rats were randomly allocated to 5 groups receiving 20 or 30 Gy of proton or photon irradiation. Pathomorphological neuronal damage in the hippocampus was assessed using Hematoxylin-eosin (HE) staining. The expression level of NeuN, Nestin, Caspase-3, Olig2, CD68 and CD45 were determined by immunohistochemistry (IHC). The RBE range established by comparing the effects of proton and photon irradiation at equivalent biological outcomes. Proton20Gy induced more severe damage to neurons than photon20Gy, but showed no difference compared to photon30Gy. The RBE of neuron was determined to be 1.65. Similarly, both proton20Gy and proton30Gy resulted in more inhibition of oligodendrocytes and activation of microglia in the hippocampal regions than photon20Gy and photon30Gy. However, the expression of Olig2 was higher and CD68 was lower in the proton20Gy group than in the photon30Gy group. The RBE of oligodendrocyte and microglia was estimated to be between 1.1 to 1.65. For neural stem cells (NSCs) and immune cells, there were no significant difference in the expression of Nestin and CD45 between proton and photon irradiation (both 20 and 30 Gy). Therefore, the RBE for NSCs and immune cell was determined to be 1.1. These findings highlight the varying RBE values of different cells in the hippocampus in vivo. Moreover, the actual RBE of the hippocampus may be higher than 1.1, suggesting that using as RBE value of 1.1 in clinical practice may underestimate the toxicities induced by proton radiation.

Analysis of heart displacement during thoracic radiotherapy based on electrocardiograph-gated 4-dimensional magnetic resonance imaging.

Background: Periodic cardiac movement may expose the heart to radiation field induced damage, leading to radiation-induced heart disease (RIHD). Studies have proven that delineation of the heart based on planning CT fails to show the real margin of the substructures and a compensatory margin should be applied. The purpose of this study was to quantify the dynamic changes and the compensatory extension range by breath-hold and electrocardiogram gated 4-dimensional magnetic resonance imaging (4D-MRI), which had the advantage of discriminating soft tissues. Methods: Eventually, 15 patients with oesophageal or lung cancers were enrolled, including one female and nine males aged from 59 to 77 years from December 10th, 2018, to March 4th, 2020. The displacement of the heart and its substructures was measured through a fusion volume and the compensatory expansion range was calculated by expending the boundary on the planning CT to that of the fusion volume. The differences were tested through the Kruskal-Wallis H test and were considered significant at a two-side P<0.05. Results: The extent of movement of heart and its substructures during one cardiac cycle were approximately 4.0-26.1 millimetre (mm) in anterior-posterior (AP), left-right (LR), and cranial-caudal (CC) axes, and the compensatory margins should be applied to planning CT by extending the margins by 1.7, 3.6, 1.8, 3.0, 2.1, and 2.9 centimetres (cm) for pericardium; 1.2, 2.5, 1.0, 2.8, 1.8, and 3.3 cm for heart; 3.8, 3.4, 3.1, 2.8, 0.9, and 2.0 cm for interatrial septum; 3.3, 4.9, 2.0, 4.1, 1.1, and 2.9 cm for interventricular septum; 2.2, 3.0, 1.1, 5.3, 1.8, and 2.4 cm for left ventricular muscle (LVM); 5.9, 3.4, 2.1, 6.1, 5.4, and 3.6 cm for antero-lateral papillary muscle (ALPM); and 6.6, 2.9, 2.6, 6.6, 3.9, and 4.8 cm for postero-medial papillary muscle (PMPM) in anterior, posterior, left, right, cranial, and caudal directions, respectively. Conclusions: Periodic cardiac activity causes obvious displacement of the heart and its substructures, and the motion amplitude of substructures differs. Extending a certain margin as the compensatory extension to represent the organs at risk (OAR) and then limiting the dose-volume parameters could be performed in clinical practice.

Topologically preserved registration of 3D CT images with deep networks.

Objective. Computed Tomography (CT) image registration makes fast and accurate imaging-based disease diagnosis possible. We aim to develop a framework which can perform accurate local registration of organs in 3D CT images while preserving the topology of transformation.Approach. In this framework, the Faster R-CNN method is first used to detect local areas containing organs from fixed and moving images whose results are then registered with a weakly supervised deep neural network. In this network, a novel 3D channel coordinate attention (CA) module is introduced to reduce the loss of position information. The image edge loss and the organ labelling loss are used to weakly supervise the training process of our deep network, which enables the network learning to focus on registering organs and image structures. An intuitive inverse module is also used to reduce the folding of deformation field. More specifically, the folding is suppressed directly by simultaneously maximizing forward and backward registration accuracy in the image domain rather than indirectly by measuring the consistency of forward and inverse deformation fields as usual.Main results. Our method achieves an average dice similarity coefficient (DSC) of 0.954 and an average Similarity (Sim) of 0.914 on publicly available liver datasets (LiTS for training and Sliver07 for testing) and achieves an average DSC of 0.914 and an average Sim of 0.947 on our home-built left ventricular myocardium (LVM) dataset.Significance. Experimental results show that our proposed method can significantly improve the registration accuracy of organs such as the liver and LVM. Moreover, our inverse module can intuitively improve the inherent topological preservation of transformations.