Auto-segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning.

BACKGROUND: Routinely delineating of important skeletal growth centers is imperative to mitigate radiation-induced growth abnormalities for pediatric cancer patients treated with radiotherapy. However, it is hindered by several practical problems, including difficult identification, time consumption, and inter-practitioner variability. PURPOSE: The goal of this study was to construct and evaluate a novel Triplet-Attention U-Net (TAU-Net)-based auto-segmentation model for important skeletal growth centers in childhood cancer radiotherapy, concentrating on the accuracy and time efficiency. METHODS: A total of 107 childhood cancer patients fulfilled the eligibility criteria were enrolled in the training cohort (N = 80) and test cohort (N = 27). The craniofacial growth plates, shoulder growth centers, and pelvic ossification centers, with a total of 19 structures in the three groups, were manually delineated by two experienced radiation oncologists on axial, coronal, and sagittal computed tomography images. Modified from U-Net, the proposed TAU-Net has one main branch and two bypass branches, receiving semantic information of three adjacent slices to predict the target structure. With supervised deep learning, the skeletal growth centers contouring of each group was generated by three different auto-segmentation models: U-Net, V-Net, and the proposed TAU-Net. Dice similarity coefficient (DSC) and Hausdorff distance 95% (HD95) were used to evaluate the accuracy of three auto-segmentation models. The time spent on performing manual tasks and manually correcting auto-contouring generated by TAU-Net was recorded. The paired t-test was used to compare the statistical differences in delineation quality and time efficiency. RESULTS: Among the three groups, including craniofacial growth plates, shoulder growth centers, and pelvic ossification centers groups, TAU-Net had demonstrated highly acceptable performance (the average DSC = 0.77, 0.87, and 0.83 for each group; the average HD95 = 2.28, 2.07, and 2.86 mm for each group). In the overall evaluation of 19 regions of interest (ROIs) in the test cohort, TAU-Net had an overwhelming advantage over U-Net (63.2% ROIs in DSC and 31.6% ROIs in HD95, p = 0.001-0.042) and V-Net (94.7% ROIs in DSC and 36.8% ROIs in HD95, p = 0.001-0.040). With an average time of 52.2 min for manual delineation, the average time saved to adjust TAU-Net-generated contours was 37.6 min (p < 0.001), a 72% reduction. CONCLUSIONS: Deep learning-based models have presented enormous potential for the auto-segmentation of important growth centers in pediatric skeleton, where the proposed TAU-Net outperformed the U-Net and V-Net in geometrical precision for the majority status.

Dosimetric predictors and Lyman normal tissue complication probability model of hematological toxicity in cervical cancer patients with treated with pelvic irradiation.

PURPOSE: To identify dosimetric parameters associated with acute hematological toxicity (HT) and identify the corresponding normal tissue complication probability (NTCP) model in cervical cancer patients receiving helical tomotherapy (Tomo) or fixed-field intensity-modulated radiation therapy (ff-IMRT) in combination with chemotherapy, that is, concurrent chemoradiotherapy (CCRT) using the Lyman-Kutcher-Burman normal tissue complication probability (LKB-NTCP) model. METHODS: Data were collected from 232 cervical cancer patients who received Tomo or ff-IMRT from 2015 to 2018. The pelvic bone marrow (PBM) (including the ilium, pubes, ischia, acetabula, proximal femora, and lumbosacral spine) was contoured from the superior boundary (usually the lumbar 5 vertebra) of the planning target volume (PTV) to the proximal end of the femoral head (the lower edge of the ischial tubercle). The parameters of the LKB model predicting ≥grade 2 hematological toxicity (Radiation Therapy Oncology Group [RTOG] grading criteria) (TD50 (1), m, and n) were determined using maximum likelihood analyses. Univariate and multivariate logistic regression analyses were used to identify correlations between dose-volume parameters and the clinical factors of HT. RESULTS: In total, 212 (91.37%) patients experienced ≥grade 2 hematological toxicity. The fitted normal tissue complication probability model parameters were TD50 (1) = 38.90 Gy (95%CI, [36.94, 40.96]), m = 0.13 (95%CI [0.12, 0.16]), and n = 0.04 (95%CI [0.02, 0.05]). Per the univariate analysis, the NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023), maximal PBM dose (p = 0.01), mean PBM dose (p = 0.021), radiation dose (p = 0.001), and V16-53 (p < 0. 05) were associated with ≥grade 2 HT. The NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023; AUC = 0.87), V16, V17, and V18 ≥ 79.65%, 75.68%, and 72.65%, respectively (p < 0.01, AUC = 0.66∼0.68), V35 and V36 ≥ 30.35% and 28.56%, respectively (p < 0.05; AUC = 0.71), and V47 ≥ 13.43% (p = 0.045; AUC = 0.80) were significant predictors of ≥grade 2 hematological toxicity from the multivariate logistic regression analysis. CONCLUSIONS: The volume of the PBM of patients treated with concurrent chemoradiotherapy and subjected to both low-dose (V16-18 ) and high-dose (V35,36 and V47 ) irradiation was associated with hematological toxicity, depending on the fractional volumes receiving the variable degree of dosage. The NTCP were stronger predictors of toxicity than V16-18 , V35, 36 , and V47 . Hence, avoiding radiation hot spots on the PBM could reduce the incidence of severe HT.

The Capabilities and Characteristics of Helical Tomotherapy and Co-Planar Dual Arcs Volumetric-Modulated arc Therapy Associated with Hippocampal Sparing During Prophylactic Cranial Irradiation.

Objective: To investigate the features of helical tomotherapy and co-planar dual Arcs volumetric-modulated arc therapy during prophylactic cranial irradiation associated with bilateral hippocampal tissue sparing. Materials and methods: Helical tomotherapy and co-planar dual arcs volumetric-modulated arc therapy treatment plans were generated with a dose of 30 Gy/10 fractions in 16 patients treated with prophylactic cranial irradiation. The dose to the bilateral hippocampal tissues, organs at risk, and planning target volume were determined when the average dose of bilateral hippocampal tissues was reduced by approximately 4 Gy as an observation point. Changes in dosimetry when sparing the bilateral hippocampal tissues were determined for both modalities. Results: When bilateral hippocampal tissues were restricted to 8 Gy, D40%mean-bilateral hippocampal tissues = 7.64 ± 0.41 Gy in helical tomotherapy, while D40%mean-bilateral hippocampal tissues = 10.96 ± 0.38 Gy in co-planar dual arcs volumetric-modulated arc therapy volumetric-modulated arc therapy. Helical tomotherapy was associated with significantly lower doses to organs at risk, including Dmean-bilateral hippocampal tissues (P = .03), D98%-bilateral hippocampal tissues (P = .01), D2%-bilateral hippocampal tissues (P = .01), Dmean-inner ear (P = .02), Dmean-parotid glands (P = .02), Dmax-lens (P = .02), and Dmax-brainstem (P = .02), but not Dmax-optic nerves (P = .87). Helical tomotherapy provided better target coverage, with lower average D2%-PTV (P = .02), higher average D98%-PTV (P = .02), and better conformal index (0.87 vs 0.84, P = .02) and homogeneity index (0.15 vs 0.21, P = .05). With smaller bilateral hippocampal tissues doses, the planning target volume dose changed across 3 dosimetry regions for both modalities; the plateau region (>20.0 Gy for helical tomotherapy versus >16.0 Gy for co-planar dual arcs volumetric-modulated arc therapy), gradient region (20.0-12.0 Gy vs 16.0-11.0 Gy), and falling region (<12.0 Gy vs <11.0 Gy). The average delivery duration of helical tomotherapy was almost 7.7 times longer than that of co-planar dual arcs volumetric-modulated arc therapy. Conclusions: Helical tomotherapy was better at sparing the bilateral hippocampal tissues and organs at risk and had better target coverage but a significantly longer treatment duration than co-planar dual arcs volumetric-modulated arc therapy. Further dose decreases in the bilateral hippocampal tissues would yield worse target dose coverage.

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

Effects of different CT value assignment methods on dose calculations in radiotherapy planning for brain metastases.

BACKGROUND: To study the effects of different CT value assignment methods on the dose calculations in radiotherapy plans for brain metastases, this study will provide a reference for radiotherapy planning design based on MR images. METHODS: All fifty recruited patients underwent CT and MR simulated localization the same day as, but prior to, three dimensional conformal radiation therapy (3D-CRT) or intensity modulated radiation therapy (IMRT) for brain metastases. After rigid registration of both the CT and MR images, the main tissues and organs were delineated on the CT and MR images. The average CT value of each tissue or organ was calculated. Three groups of pseudo-CT were generated by three CT value assignment methods: (I) the whole tissue was assigned 140 HU; (II) cavity, bone and other tissues were assigned -700, 700 and 20 HU, respectively; (III) tissue- and organ-specific CT values were given. The dose distribution was recalculated based on the three groups of pseudo-CT to obtain Plan2, Plan3 and Plan4, accordingly. The resultant radiotherapy plans were considered the original plan (Plan1). Then, the dosimetric differences between these three plans and Plan1 were compared. RESULTS: The average pseudo-CT values of bone and cavity were 731.7±69.3 and -725.5±26.1 HU, respectively. The range of average soft-tissue CT values was from -70 to 70 HU. The dose distribution between Plan1 and Plan2, Plan3 or Plan4 showed some differences, and the differences decreased in turn. The differences in the maximum dose of the lenses can reach 5.0%, 1.5% and 1.2%, respectively, while the differences in other dose parameters (maximum dose, mean dose and D 98% to the PTV, D 5% of the brainstem, and maximum dose of the brainstem, corpus callosum, left eye, right eye) were basically less than 2.0%, 1.2% and 0.8%, respectively. This shows that in the CT value assignment method, the dose calculation error can be greatly reduced by assigning the value to the bone and cavity separately, and if the different soft tissues are distinguished, the error of the dose calculation can be further reduced by more than 30%. In the pixel-by-pixel dosimetric comparison, the areas of more than 1% dose difference between Plan1 and Plan3 as well as Plan4 were mainly distributed near skin while those between Plan1 and Plan2 were mainly distributed at the bone, cavity, bone and soft tissues junction, and the skin near the field. CONCLUSIONS: In summary, a scheme for assigning specific CT values to MRI-based radiotherapy is established. The scheme will provide patients with a dose-free radiotherapy plan. Through the calculation of the differences between the new plans and the old plan, it is found that our scheme can basically control the dose error below 0.8% to meet the clinical requirements.

User Access Management Based on Network Pricing for Social Network Applications.

Social applications play a very important role in people's lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network's income.