Aplasia cutis congenita (ACC) is a term describing absence of skin at birth. ACC is a rare cutaneous finding, often noted with no other physical abnormalities. The etiology of ACC varies, and there are likely several causes for its development. ACC can be located anywhere on the body. Its clinical appearance and location can alert the clinician to other potential abnormalities and associations. This discussion covers the diagnosis of ACC and its subtypes and associations in order to provide a pragmatic, clinically relevant and patient-centered approach to evaluation and treatment.
At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected.
This study proposes and simulates an inverse treatment planning and a continuous dose delivery approach for the Leksell Gamma Knife (LGK, Elekta, Stockholm, Sweden) which we refer to as "Tomosurgery." Tomosurgery uses an isocenter that moves within the irradiation field to continuously deliver the prescribed radiation dose in a raster-scanning format, slice by slice, within an intracranial lesion. Our Tomosurgery automated (inverse) treatment planning algorithm utilizes a two-stage optimization strategy. The first stage reduces the current three-dimensional (3D) treatment planning problem to a series of more easily solved 2D treatment planning subproblems. In the second stage, those 2D treatment plans are assembled to obtain a final 3D treatment plan for the entire lesion. We created Tomosurgery treatment plans for 11 patients who had already received manually-generated LGK treatment plans to treat brain tumors. For the seven cases without critical structures (CS), the Tomosurgery treatment plans showed borderline to significant improvement in within-tumor dose standard deviation (STD) (p <0.058, or p <0.011 excluding case 2) and conformality (p < 0.042), respectively. In three of the four cases that presented CS, the Tomosurgery treatment plans showed no statistically significant improvements in dose conformality (p <0.184), and borderline significance in improving within-tumor dose homogeneity (p <0.054); CS damage measured by V20 or V30 (i.e., irradiated CS volume that receives > or =20% or > or =30% of the maximum dose) showed no significant improvement in the Tomosurgery treatment plans (p<0.345 and p <0.423, respectively). However, the overall CS dose volume histograms were improved in the Tomosurgery treatment plans. In addition, the LGK Tomosurgery inverse treatment planning required less time than standard of care, forward (manual) LGK treatment planning (i.e., 5-35 min vs 1-3 h) for all 11 cases. We expect that LGK Tomosurgery will speed treatment planning and improve treatment quality, especially for large and/or geometrically complex lesions. However, using only 4 mm collimators could greatly increase treatment plan delivery time for a large brain lesion. This issue is subject to further investigation.
Purpose: The flattening filter free (FFF) photon beams offer high dose rate, greatly speeding up treatment delivery. This work aims to evaluate the impact of FFF beam on treatment planning for both static‐gantry (sIMRT) and rotational IMRT (rIMRT). Methods: Both FFF and flattening filtered (FF) photon beams from a Siemens Artiste machine, calibrated to deliver 1 cGy/MU at dmax with a 10×10 cm, 2 field, were used in our study. Both sIMRT and rIMRT plans were generated with either FFF or FF beams for 10 previously treated cases, using a planning system based on direct aperture optimization algorithm (Panther, Prowess). The plans were compared based on dose‐volume histograms with parameters including dose uniformity for planning target volume (PTV) and equivalent uniform dose for organs at risk (OAR), as well as monitor unit (MU) numbers. Results: In general, the plan qualities for rIMRT plans are equal to or slightly better than those for sIMRT, while the plan quality of a FFF‐beam plan is slightly worse than that for the corresponding FF‐beam plan. The plan quality of a FFF‐beam rIMRT plan is close to that for the FF‐beam sIMRT plan, and the former has slightly worse dose uniformity in PTV but slightly better OAR sparing. Both rotational delivery and the use of FFF‐beam require more MUs, with the latter to more extent. The MU increase can be up to 34% for large PTV and complicated OAR configurations. However, FFF‐beam rIMRT has the advantages of high delivery efficiency with overall treatment time reduced by up to 30%. Conclusions: Plan quality degrade associated with FFF beams can be compromised with rotational IMRT technique. The overall treatment time can be reduced with FFF beams, even though the total MUs are increased. FFF beams are more advantageous for a small sized target or less complicated OAR geometry.
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