Semi-supervised auto-segmentation method for pelvic organ-at-risk in magnetic resonance images based on deep-learning.

BACKGROUND AND PURPOSE: In radiotherapy, magnetic resonance (MR) imaging has higher contrast for soft tissues compared to computed tomography (CT) scanning and does not emit radiation. However, manual annotation of the deep learning-based automatic organ-at-risk (OAR) delineation algorithms is expensive, making the collection of large-high-quality annotated datasets a challenge. Therefore, we proposed the low-cost semi-supervised OAR segmentation method using small pelvic MR image annotations. METHODS: We trained a deep learning-based segmentation model using 116 sets of MR images from 116 patients. The bladder, femoral heads, rectum, and small intestine were selected as OAR regions. To generate the training set, we utilized a semi-supervised method and ensemble learning techniques. Additionally, we employed a post-processing algorithm to correct the self-annotation data. Both 2D and 3D auto-segmentation networks were evaluated for their performance. Furthermore, we evaluated the performance of semi-supervised method for 50 labeled data and only 10 labeled data. RESULTS: The Dice similarity coefficient (DSC) of the bladder, femoral heads, rectum and small intestine between segmentation results and reference masks is 0.954, 0.984, 0.908, 0.852 only using self-annotation and post-processing methods of 2D segmentation model. The DSC of corresponding OARs is 0.871, 0.975, 0.975, 0.783, 0.724 using 3D segmentation network, 0.896, 0.984, 0.890, 0.828 using 2D segmentation network and common supervised method. CONCLUSION: The outcomes of our study demonstrate that it is possible to train a multi-OAR segmentation model using small annotation samples and additional unlabeled data. To effectively annotate the dataset, ensemble learning and post-processing methods were employed. Additionally, when dealing with anisotropy and limited sample sizes, the 2D model outperformed the 3D model in terms of performance.

Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.

BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy. MATERIALS AND METHODS: Conventional T2-weighted MR and CT images were acquired from 90 rectal cancer patients at Peking University People's Hospital and 19 patients in public datasets. This study proposed a new model combining contrastive learning loss and consistency regularization loss to enhance the generalization of model for multi-center pelvic MRI-to-CT synthesis. The CT-to-sCT image similarity was evaluated by computing the mean absolute error (MAE), peak signal-to-noise ratio (SNRpeak), structural similarity index (SSIM) and Generalization Performance (GP). The dosimetric accuracy of synthetic CT was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed. RESULTS: Our model presented excellent generalization with a GP of 0.911 on unseen datasets and outperformed the plain CycleGAN, where MAE decreased from 47.129 to 42.344, SNRpeak improved from 25.167 to 26.979, SSIM increased from 0.978 to 0.992. The dosimetric analysis demonstrated that most of the relative differences in dose and volume histogram (DVH) indicators between synthetic CT and real CT were less than 1%. CONCLUSION: The proposed model can generate accurate synthetic CT in multi-center datasets from T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of an MRI-only workflow for patients with rectal cancer.

PGR-KITLG signaling drives a tumor-mast cell regulatory feedback to modulate apoptosis of breast cancer cells.

The immune microenvironment constructed by tumor-infiltrating immune cells and the molecular phenotype defined by hormone receptors (HRs) have been implicated as decisive factors in the regulation of breast cancer (BC) progression. Here, we found that the infiltration of mast cells (MCs) informed impaired prognoses in HR(+) BC but predicted improved prognoses in HR(-) BC. However, molecular features of MCs in different BC remain unclear. We next discovered that HR(-) BC cells were prone to apoptosis under the stimulation of MCs, whereas HR(+) BC cells exerted anti-apoptotic effects. Mechanistically, in HR(+) BC, the KIT ligand (KITLG), a major mast cell growth factor in recruiting and activating MCs, could be transcriptionally upregulated by the progesterone receptor (PGR), and elevate the production of MC-derived granulin (GRN). GRN attenuates TNFα-induced apoptosis in BC cells by competitively binding to TNFR1. Furthermore, disruption of PGR-KITLG signaling by knocking down PGR or using the specific KITLG-cKIT inhibitor iSCK03 potently enhanced the sensitivity of HR(+) BC cells to MC-induced apoptosis and exerted anti-tumor activity. Collectively, these results demonstrate that PGR-KITLG signaling in BC cells preferentially induces GRN expression in MCs to exert anti-apoptotic effects, with potential value in developing precision medicine approaches for diagnosis and treatment.