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 Notably, the joint omics approach superseded the performance of solely radiomic or clinical model.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.

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.

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.

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.

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.

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.

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%.

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.

Three-dimensional arterial spin labeling-guided dose painting radiotherapy for non-enhancing low-grade gliomas.

PURPOSE: To investigate the feasibility and dosimetric characteristics of dose painting for non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL). MATERIALS AND METHODS: Eighteen patients with NE-LGGs were enrolled. 3D-ASL, T2 fluid-attenuated inversion recovery (T2 Flair) and contrast-enhanced T1-weighted magnetic resonance images were obtained. The gross tumor volume (GTV) was delineated on the T2 Flair. The hyper-perfusion region of the GTV (GTV-ASL) was determined by 3D-ASL, and the GTV-SUB was obtained by subtracting the GTV-ASL from the GTV. The clinical target volume (CTV) was created by iso-tropically expanding the GTV by 1 cm. The planning target volume (PTV), PTV-ASL were obtained by expanding the external margins of the CTV, GTV-ASL, respectively. PTV-SUB was generated by subtracting PTV-ASL from PTV. Three plans were generated for each patient: a conventional plan (plan 1) without dose escalation delivering 95-110% of 45-60 Gy in 1.8-2 Gy fractions to the PTV and two dose-painting plans (plan 2 and plan 3) with dose escalating by 10-20% (range, 50-72 Gy) to the PTV-ASL based on plan 1. The plan 3 was obtained from plan 2 without the maximum dose constraint. The dosimetric differences among the three plans were compared. RESULTS: The volume ratio of the PTV-ASL to the PTV was (23.49 ± 11.94)% (Z = - 3.724, P = 0.000). Compared with plan 1, D2%, D98% and Dmean of PTV-ASL increased by 14.67%,16.17% and 14.31% in plan2 and 19.84%,15.52% and 14.27% in plan3, respectively (P < 0.05); the D2% of the PTV and PTV-SUB increased by 11.89% and 8.34% in plan 2, 15.89% and 8.49% in plan 3, respectively (P < 0.05). The PTV coverages were comparable among the three plans (P > 0.05). In plan 2 and plan 3, the conformity indexes decreased by 18.60% and 12.79%; while the homogeneity index increased by 1.43 and 2 times (P < 0.05). Compared with plan 1, the D0.1 cc of brain stem and Dmax of optic chiasma were slightly increased in plan 2 and plan 3, and the absolute doses met the dose constraint. The doses of the other organs at risk (OARs) were similar among the three plans (P > 0.05). CONCLUSION: The dose delivered to hyper-perfusion volume derived from 3D-ASL can increased by 10-20% while respecting the constraints to the OARs for NE-LGGs, which provides a basis for future individualized and precise radiotherapy, especially if the contrast agent cannot be injected or when contrast enhancement is uncertain.

A novel approach for dose painting radiotherapy of brain metastases guided by mr perfusion images.

Purpose: To investigate the feasibility and dosimetric index features of dose painting guided by perfusion heterogeneity for brain metastasis (BMs) patients. Methods: A total of 50 patients with single BMs were selected for this study. CT and MR simulation images were obtained, including contrast-enhanced T1-weighted images (T1WI+C) and cerebral blood flow (CBF) maps from 3D-arterial spin labeling (ASL). The gross tumor volume (GTV) was determined by fusion of CT and T1WI+C images. Hypoperfused subvolumes (GTVH) with less than 25% of the maximum CBF value were defined as the dose escalation region. The planning target volume (PTV) and PTVH were calculated from GTV and GTVH respectively. The PTVN was obtained by subtracting PTVH from PTV, and conventional dose was given. Three kinds of radiotherapy plans were designed based on the CBF values. Plan 1 was defined as the conventional plan with an arbitrary prescription dose of 60 Gy for PTV. For dose painting, Plan 2 and Plan 3 escalated the prescription dose for PTVH to 72 Gy based on Plan 1, but Plan 3 removed the maximum dose constraint. Dosimetric indices were compared among the three plans. Results: The mean GTV volume was 34.5 (8.4-118.0) cm3, and mean GTVH volume was 17.0 (4.5-58.3) cm3, accounting for 49.3% of GTV. Both conventional plan and dose painting plans achieved 98% target coverage. The conformity index of PTVH were 0.44 (Plan1), 0.64 and 0.72 (Plan 2 and Plan 3, P<0.05). Compared to Plan 1, the D2%, D98% and Dmean values of the PTVH escalated by 20.50%, 19.32%, and 19.60% in Plan 2 and by 24.88%, 17.22% and 19.22% in Plan 3 respectively (P<0.05). In the three plans, the index of achievement value for PTVH was between 1.01 and 1.03 (P<0.05). The dose increment rates of Plan 2 and Plan 3 for each organs at risk (OARs) was controlled at 2.19% - 5.61% compared with Plan 1. The doses received by OARs did not significantly differ among the three plans (P >0.05). Conclusions: BMs are associated with significant heterogeneity, and effective escalation of the dose delivered to target subvolumes can be achieved with dose painting guided by 3D-ASL without extra doses to OARs.

Dosimetric evaluation of bone marrow sparing in proton radiotherapy for cervical cancer guided by MR functional imaging.

BACKGROUND: To segment the pelvic active bone marrow (PABM) using magnetic resonance (MR) functional imaging and investigate the feasibility and dosimetric characteristics of cervical cancer proton radiotherapy for active bone marrow (ABM) sparing. METHODS: We collected CT and MR simulation images of 33 patients with cervical cancer retrospectively. The PBM was contoured on the MRI FatFrac images; the PBM was divided into high-active bone marrow (ABM-high) and low-active bone marrow based on the fat content of the PBM. Four radiotherapy plans were created for each patient, which included intensity-modulated photon therapy (IMRT), bone marrow sparing IMRT (IMRT-BMS), intensity-modulated proton therapy (IMPT), and bone marrow sparing IMPT (IMPT-BMS). The dosimetric differences among the four plans were compared. RESULTS: The ABM-high volume in the enrolled patients accounted for 45.2% of the total ABM volume. The target coverage was similar among the four radiotherapy plans. IMRT-BMS, IMPT, and IMPT-BMS reduced the Dmean of ABM-high by 16.6%, 14.2%, and 44.5%, respectively, compared to the Dmean of IMRT (p < 0.05). IMPT-BMS had the best protective effect on the bone marrow. Compared to IMRT, the volume of ABM-high receiving an irradiation dose of 5-40 Gy decreased by 10.2%, 36.8%, 58.8%, 67.4%, 64.9%, and 44.5%, respectively (p < 0.001). CONCLUSIONS: The MR functional imaging technique helped in the grading and segmentation of PABM. MR functional image-guided proton radiotherapy for cervical cancer can achieve optimal BMS.

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.

The effect of time delay for magnetic resonance contrast-enhanced scan on imaging for small-volume brain metastases.

PURPOSE: To study the effect of different enhancement timings of magnetic resonance (MR) on small-volume brain metastases (BM) visualisation and provide a basis for the contour of tumour targets. METHOD: We prospectively enrolled 101 patients with BM who received radiotherapy. All patients underwent computed tomography (CT) and MR simulations. Contrast-enhanced MR scans at 1, 3, 5, 10, 18, and 20 min after injection of contrast medium were performed. The tumour target was determined on MR images at different enhancement times, and the differences of tumour target volume, maximum diameter, and MR signal intensity were compared. RESULTS: (1) Of the 453 metastatic lesions, 24 (5.2 %) were not detected at 1 min and 8 (1.8 %) were not detected at 3 min; however, all metastases were detected after 5 min. The volume and maximum diameter of the 28 (6.2 %) metastases were stable at any time. (2) The average volume of metastatic lesions at 1, 3, 5, 10, 18, and 20 min was 0.09 cm3, 0.10 cm3, 0.12 cm3, 0.12 cm3, 0.13 cm3, and 0.13 cm3, respectively. Compared to 1 min, BM volume at other times increased by 13.1 %, 21.5 %, 31.6 %, 39.6 %, and 41.7 %, and the difference between the maximum and minimum volumes was statistically significant (p < 0.05). (3) The distribution of the maximum ratio of tumours to white matter mean signal intensity at different times were 39.6 %, 20 %, 14.6 %, 8.0 %, 10.4 %, and 10 %, respectively. CONCLUSION: The visualisation of small-volume BM was significantly different at different enhancement times. Our results suggest that multi-timing enhancement scans for small-volume BM should be implemented and that scanning at >10 min is essential.

Three-Dimensional Arterial Spin Labeling-Guided Sub-Volume Segmentation of Radiotherapy in Adult Non-Enhancing Low-Grade Gliomas.

Objective: The present study aimed to evaluate the feasibility of sub-volume segmentation for radiotherapy planning of adult non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL). The differences in high- and low-perfusion areas of NE-LGGs were analyzed using multi-sequence magnetic resonance imaging (MRI) radiomics. Methods: Fifteen adult patients with NE-LGGs were included in the study. MR images, including T1-weighted imaging (T1WI), T2 Propeller, T2 fluid-attenuated inversion recovery (T2 Flair), 3D-ASL, and contrast-enhanced T1WI (CE-T1WI), were obtained. The gross tumor volume (GTV) was delineated according to the hyperintensity on T2 Flair. The GTV was divided into high- and low-perfusion areas, namely GTV-ASL and GTV-SUB, respectively, based on the differences in cerebral blood flow (CBF) value. The volumes and CBF values of high- and low-perfusion areas were measured and compared. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal features of all MR maps. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic accuracy of the absolute CBFmean (aCBFmean), relative CBFmean (rCBFmean, normalized by the CBF value of the normal gray matter), and screened features in differentiating high- and low-perfusion areas. Results: Among the enrolled patients, three (20%) patients with NE-LGGs showed focal intra- and post-radiotherapy contrast enhancement within a prior high-perfusion area of 3D-ASL. The volume ratio of the GTV-ASL to the GTV was (37.08% ± 17.88)% (46.26 ± 44.51 vs. 167.46 ± 209.64 cm3, P = 0.000). The CBFmean in the high-perfusion area was approximately two times of that in the edema area or normal gray matter (66.98 ± 18.03 vs. 35.19 ± 7.75 or 33.92 ± 8.48 ml/100g/min, P = 0.000). Thirteen features were screened, seven of which were extracted from 3D-ASL. The area undercurve (AUC) values of aCBFmean, rCBFmean, and firstorder_10Percentile from 3D-ASL were more than 0.9, of which firstorder_10Percentile was the highest. Their cut-off values were 44.16 ml/100 g/min, 1.49 and 31, respectively. Conclusion: The difference in blood perfusion in the GTV can be quantified and analyzed based on 3D-ASL images for NE-LGGs, which could guide the sub-volume segmentation of the GTV. 3D-ASL should become a routine method for NE-LGGs during simulation and radiotherapy.

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.

Correlation between changes of pelvic bone marrow fat content and hematological toxicity in concurrent chemoradiotherapy for cervical cancer.

OBJECTIVES: To quantify the pelvic bone marrow (PBM) fat content changes receiving different radiation doses of concurrent chemoradiotherapy for cervical cancer and to determine association with peripheral blood cell counts. METHODS: The data of 54 patients were prospectively collected. Patients underwent MRI iterative decomposition of water and fat with echo asymmetrical and least squares estimation (IDEAL IQ) scanning at RT-Pre, RT mid-point, RT end, and six months. The changes in proton density fat fraction (PDFF%) at 5-10 Gy, 10-15 Gy, 15-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy, and > 50 Gy doses were analyzed. Spearman's rank correlations were performed between peripheral blood cell counts versus the differences in PDFF% at different dose gradients before and after treatment. RESULTS: The lymphocytes (ALC) nadirs appeared at the midpoint of radiotherapy, which was only 27.6% of RT-Pre; the white blood cells (WBC), neutrophils (ANC), and platelets (PLT) nadirs appeared at the end of radiotherapy which was 52.4%, 65.1%, and 69.3% of RT-Pre, respectively. At RT mid-point and RT-end, PDFF% increased by 46.8% and 58.5%, respectively. Six months after radiotherapy, PDFF% decreased by 4.71% under 5-30 Gy compared to RT-end, while it still increased by 55.95% compared to RT-Pre. There was a significant positive correlation between PDFF% and ANC nadirs at 5-10 Gy (r = 0.62, P = 0.006), and correlation was observed between PDFF% and ALC nadirs at 5-10 Gy (r = 0.554, P = 0.017). CONCLUSION: MRI IDEAL IQ imaging is a non-invasive approach to evaluate and track the changes of PBM fat content with concurrent chemoradiotherapy for cervical cancer. The limitation of low-dose bone marrow irradiation volume in cervical cancer concurrent chemoradiotherapy should be paid more attention to.

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.

The Study of Cerebral Blood Flow Variations during Brain Metastases Radiotherapy.

PURPOSE: The aim of this study was to investigate the cerebral blood flow (CBF) variations during brain metastases (BMs) radiotherapy (RT) applying with magnetic resonance (MR) 3D-arterial spin labeling (ASL). MATERIALS AND METHODS: A total of 26 BM patients with 54 tumors were retrospectively enrolled. MR examinations were performed before and during RT (30-50 Gy) with a total dose of 36-60 Gy (12-30 fractions) including contrast-enhanced T1-weighted, T2 Flair, and 3D-ASL images. The relationship between CBF changes and the largest cross-sectional area changes in BMs was investigated. And CBF changes in BMs, normal brain tissue, and peritumoral edema areas were analyzed under different dose gradients that were divided into 10 Gy intervals. RESULTS: The largest cross-sectional areas and CBF of 54 BMs decreased by 26.46% and 29.64%, respectively, during RT (p < 0.05), but there was no correlation between the 2 changes (p > 0.05). The rates of CBF decrease in BMs were 33.75%, 24.61%, and 27.55% at 30-40, 40-50, and >50 Gy, respectively (p < 0.05). In normal brain tissue with dose gradients of 0-10, 10-20, 20-30, 30-40, 40-50, and >50 Gy, the CBF decreased by 7.65%, 11.12%, 18.42%, 20.23%, 19.79%, and 17.89%, respectively (p < 0.05). The CBF decreases reached a maximum at 30-40 Gy in normal brain tissue as well as BMs. In contrast, the CBF decreases of peritumoral edema areas increased as the dose gradients increased. Moreover, the CBF changes of BMs were more notable than those in normal brain tissue and peritumoral edema areas. CONCLUSION: CBF changes can be feasibly assessed in different brain regions during RT based on 3D-ASL. The changes should be considered as a critical factor to determine the personal radiation dose for BMs, normal brain tissue, and peritumoral edema areas.

Radiomics Nomogram for Predicting Locoregional Failure in Locally Advanced Non-small Cell Lung Cancer Treated with Definitive Chemoradiotherapy.

RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting locoregional failure (LRF) in patients with locally advanced non-small cell lung cancer (NSCLC) treated with definitive chemoradiotherapy (CRT). MATERIALS AND METHODS: A total of 141 patients with locally advanced NSCLC treated with definitive CRT from January 2014 to December 2017 were included and divided into testing cohort (n = 100) and validation (n = 41) cohort. Radiomics features were extracted from pretreatment contrast enhanced CT. The least absolute shrinkage and selection operator logistic regression was processed to select predictive features from the testing cohort and constructed a radiomics signature. Clinical characteristics and the radiomics signature were analyzed using univariable and multivariate Cox regression. The radiomics nomogram was established with the radiomics signature and independent clinical factors. Harrell's C-index, calibration curves and decision curves were used to assess the performance of the radiomics nomogram. RESULTS: The radiomics signature, which consisted of eight selected features, was an independent factor of LRF. The clinical predictors of LRF were the histologic type and clinical stage. The radiomics nomogram combined with the radiomics signature and clinical prognostic factors showed good performance with C-indexes of 0.796 (95% confidence interval [CI]: 0.709-0.883) and 0.756 (95% CI: 0.674-0.838) in the testing and validation cohorts respectively. Additionally, the combined nomogram resulted in better performance (p < 0.001) for the estimation of LRF than the nomograms with the radiomics signature (C-index: 0.776; 95% CI: 0.686-0.866) or clinical predictors (C-index: 0.641; 95% CI: 0.542-0.740) alone. CONCLUSION: The radiomics nomogram provided the best performance for LRF prediction in patients with locally advanced NSCLC, which may help optimize individual treatments.