No abstract

Rationale: Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of non-small cell lung cancer (NSCLC). It is predominantly reported in East Asia and currently there is no standard treatment for this disease. We report a case of stage IV PPLELC that achieved pathological complete response (pCR) by neoadjuvant treatment. Patient concerns: The patient was a 46-year-old male who developed hemoptysis for about 20 ml of volume accompanied by cough and sputum after physical labor. Diagnoses: Contrast enhanced chest CT scanning showed occupation of left lower hilar area and left pleural effusion. Combined with medical history and auxiliary examination, the patient was formally diagnosed stage IV lymphoepithelioma-like carcinoma of the left lower lung (T3N0M1a pleura). Interventions: The patient was given Sintilimab combined with gemcitabine + nedaplatin chemotherapy (GP) regimen for four cycles with 3 weeks as a cycle, supplemented with antiemetics and stomach protection drugs to reduce chemotherapy-related side effects. Outcomes: After 4 cycles of treatment, the patient's left lung lesion has been markedly reduced and the left pleural effusion has also been significantly absorbed. Remarkably, surgical biopsies found no cancer cells in the lesion site and postoperative pathology showed complete pathological remission (pCR). Lessons: We reported a case of PPLELC that is sensitive to neoadjuvant treatment, showing excellent effectiveness and safety and achieving pCR.

In order to study the realization of medical image restoration, this study mainly adopts blind equalization algorithm to analyze medical images, and observes the improvement effect of blind equalization technology on medical images. In the process of medical image formation, it is unavoidable to be affected by point spread function, which leads to image degradation and brings great difficulties to diagnosis, and the results of degradation are often unpredictable. The results show that the blind restoration algorithm can restore the image when the degradation process of the medical image is uncertain, which makes the medical image clearer and more accurate, brings great convenience to the diagnosis, and also reduces the diagnostic errors caused by the unclear image.

The purpose of this study is to explore the value of CT (computed tomography) continuous scanning by MIMICS (Materiaise's interactive medical image control system) in segmentation modeling of lung cancer images and to analyze the process of building a three-dimensional finite element model of lung cancer quickly and accurately. In this study, MIMICS software is used to conduct data collection of routine CT continuous image for 12 patients diagnosed with lung cancer, and a database is established. The image is divided according to a specific rule, such as grayscale, space, texture, geometric shape and other characteristics, to form a series of meaningful regions. Software MIMICS17.0 is used to perform visualization segmentation, extraction, and 3d rendering to images, followed by data transformation. Because the 3d images constructed by MIMICS software have the advantages of strong editability and wide subsequent application, the lung cancer model images constructed in this study are lifelike in shape, with high geometric similarity and strong sense of solidity, truly reflecting the morphological characteristics of lung cancer and the spatial relationship between adjacent structures. Therefore, by transparently treating the lungs, the internal tumor, bronchial system and vascular system can be observed directly, thereby better presenting the spatial location of the lung tumor. However, due to the limitations of the software itself, the XYZ data of lung of space-occupying lesions could not be output by the program, which needs to be further studied and verified.

In order to explore the three-dimensional reconstruction of lung cancer CT images by MIMICS software, the purpose of enhancing the effect of the lesion area is realized, and the visualization model can be realized, which can effectively assist the early diagnosis of lung cancer and the surgical treatment of lung cancer. Using lung CT scan data from lung cancer patients, semi-automated segmentation methods are used to segment the lung tumors in MIMICS software to create an individualized lung cancer model. MIMICS software accurately and efficiently distributes the model according to the gray values of different tissues, and successfully establishes a human body chest finite element model with realistic shape and accurate structure. The model includes chest skin, bones, lungs, pulmonary arteries, pulmonary veins, and tracheobronchial trees, providing precise model support for finite element analysis. This study successfully constructs a three-dimensional digital model of individualized lung cancer. The model has high geometric similarity and strong stereoscopic effect. It truly reflects the morphological characteristics of lung cancer and the spatial relationship between adjacent structures, which makes the imaging effect of lung cancer lesions more prominent. By moving, separating, combining, and revealing the model, multi-angle observation and measurement can be performed, which facilitates preoperative planning and shortens the operation time.