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.

The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models. A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model’s accuracy. The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators. The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.

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.

To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images. 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). 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

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.

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.