ObjectiveTo summarize the treatment strategies and clinical experiences of 5 cases of giant plexiform neurofibromas (PNF) involving the head, face, and neck. MethodsBetween April 2021 and May 2023, 5 patients with giant PNFs involving the head, face, and neck were treated, including 1 male and 4 females, aged 6-54 years (mean, 22.4 years). All tumors showed progressive enlargement, involving multiple regions such as the maxillofacial area, ear, and neck, significantly impacting facial appearance. Among them, 3 cases involved tumor infiltration into deep tissues, affecting development, while 4 cases were accompanied by hearing loss. Imaging studies revealed that all 5 tumors predominantly exhibited an invasive growth pattern, in which 2 and 1 also presenting superficial and displacing pattern, respectively. The surgical procedure followed a step-by-step precision treatment strategy based on aesthetic units, rather than simply aiming for maximal tumor resection in a single operation. Routine preoperative embolization of the tumor-feeding vessels was performed to reduce bleeding risk, followed by tumor resection combined with reconstructive surgery. Results All 5 patients underwent 1-3 preoperative embolization procedures, with no intraoperative hemorrhagic complications reported. Four patients required intraoperative blood transfusion. A total of 10 surgical procedures were performed across the 5 patients. One patient experienced early postoperative flap margin necrosis due to ligation for hemostasis; however, the incisions in the remaining patients healed without complications. All patients were followed up for a period ranging from 6 to 36 months, with a mean follow-up duration of 21.6 months. No significant tumor recurrence was observed during the follow-up period. Conclusion For patients with giant PNF involving the head, face, and neck, precision treatment strategy can effectively control surgical risks and improve the standard of aesthetic reconstruction. This approach enhances overall treatment outcomes by minimizing complications and optimizing functional and cosmetic results.
With the advancement of thyroid tumor treatment concepts and the progress of standardized treatment processes nationwide, the 5-year survival rate of thyroid tumors in China has risen from 67.5% in 2003 to 84.3% in 2015. As China has been continuously enriching its treatment options for advanced thyroid cancer in recent years, gradually improving the standardized treatment system for early and intermediate thyroid cancer, enhancing multidisciplinary collaboration methods and concepts, and regularizing scientific statistics, the survival rate of thyroid tumors continues to improve. We still need to consider the future development direction and core driving force of China’s thyroid discipline, correctly view the “prosperous” stage of domestic thyroid discipline development, and actively review the future development direction of China’s thyroid discipline.
ObjectiveTo summarize the application of radiomics in colorectal cancer.MethodsRelevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.ResultsRadiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.ConclusionRadiomics is an important part of precision medical imaging for colorectal cancer.
Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.
Sepsis is a critical condition. The key factor affecting the survival of patient is whether standard treatment can be obtained timely. Because of the complexity of its pathogenesis and high heterogeneity, there is no special diagnosis method currently. Early identification is difficult. Delayed diagnosis and treatment is closely related to the mortality of patients. With the continuous updating of the guidelines, sepsis has been included in the “time window” disease, putting forward a great challenge to the early screening and evaluation of sepsis. This article aims to review the application of Sepsis-Related Organ Failure Assessment, sepsis biomarkers and artificial intelligence algorithms in early screening and evaluation of sepsis, so as to provide guidance tools for timely starting standardized treatment of sepsis.
Objective To evaluate effects of three-dimensional (3D) visualized reconstruction technology on short-term benefits of different extent of resection in treating hepatic alveolar echinococcosis (HAE) as well as some disadvantages. Methods One hundred and fifty-two patients with HAE from January 2014 to December 2016 in the Department Liver Surgery, West China Hospital of Sichuan University were collected, there were 80 patients with ≥4 segments and 72 patients with ≤3 segments of liver resection among these patients, which were designed to 3D reconstruction group and non-3D reconstruction group according to the preference of patients. The imaging data, intraoperative and postoperative indicators were recorded and compared. Results The 3D visualized reconstructions were performed in the 79 patients with HAE, the average time of 3D visualized reconstruction was 19 min, of which 13 cases took more than 30 min and the longest reached 150 min. The preoperative predicted liver resection volume of the 79 patients underwent the 3D visualized reconstruction was (583.6±374.7) mL, the volume of intraoperative actual liver resection was (573.8±406.3) mL, the comparison of preoperative and intraoperative data indicated that both agreed reasonably well (P=0.640). Forty-one cases and 38 cases in the 80 patients with ≥4 segments and 72 patients with ≤3 segments of liverresection respectively were selected for the 3D visualized reconstruction. For the patients with ≥4 segments of liver resection, the operative time was shorter (P=0.021) and the blood loss was less (P=0.047) in the 3D reconstruction group as compared with the non-3D reconstruction group, the status of intraoperative blood transfusion had no significant difference between the 3D reconstruction group and the non-3D reconstruction group (P=0.766). For the patients with ≤3 segments of liver resection, the operative time, the blood loss, and the status of intraoperative blood transfusion had no significant differences between the 3D reconstruction group and the non-3D reconstruction group (P>0.05). For the patients with ≥4 segments or ≤3 segments of liver resection, the laboratory examination results within postoperative 3 d, complications within postoperative 90 d, and the postoperative hospitalization time had no significant differences between the 3D reconstruction group and the non-3D reconstruction group (P>0.05). Conclusion 3D visualized reconstruction technology contributes to patients with HAE ≥4 segments of liver resection, it could reduce intraoperative blood loss and shorten operation time, but it displays no remarkable benefits for ≤3 segments of liver resection.
Along with the popularity of low-dose computed tomography lung cancer screening, an increasing number of early-stage lung cancers are detected. Radical lobectomy with systematic nodal dissection (SND) remains the standard-of-care for operable lung cancer patients. However, whether SND should be performed on non-metastatic lymph nodes remains controversy. Unnecessary lymph node dissection can increase the difficulty of surgery while also causing additional surgical damage. In addition, non-metastatic lymph nodes have been recently reported to play a key role in immunotherapy. How to reduce the surgical damage of mediastinal lymph node dissection for early-stage lung cancer patients is pivotal for modern concept of "minimally invasive surgery for lung cancer 3.0". The selective mediastinal lymph node dissection strategy aims to dissect lymph nodes with tumor metastasis while preserving normal mediastinal lymph nodes. Previous studies have shown that combination of specific tumor segment site, radiology and intraoperative frozen pathology characteristics can accurately predict the pattern of mediastinal lymph node metastasis. The personalized selective mediastinal lymph node dissection strategy formed from this has been successfully validated in a recent prospective clinical trial, providing an important basis for early-stage lung cancer patients to receive more personalized selective lymph node dissection with "precision surgery" strategies.
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.
ObjectiveTo review the progress of radiomics in the field of colorectal cancer in recent years and summarize its value in the imaging diagnosis of colorectal cancer.MethodsEighty English and seven Chinese articles were retrieved through PUBMED, OVID, CNKI, Weipu and Wanfang. The structure and content of these literatures were classified and analyzed.ResultsIn five studies predicting the preoperative stages of colorectal cancer based on CT radiomics, the area under curve (AUC) ranged from 0.736 to 0.817; in two studies predicting the preoperative stages of colorectal cancer based on MRI radiomics, the AUC were 0.87 and 0.827 respectively. In two studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on CT, the AUC were 0.79 and 0.72 respectively; in four studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on MRI, the AUC ranged from 0.84 to 0.979. In one study evaluating the sensitivity of neoadjuvant chemotherapy based on MRI radiomics, the AUC was 0.79. In one study predicting the postoperative survival rate based on MRI radiomics, the AUC value of the final model was 0.827. In one study, the accuracy of the model based on PET/CT radiomics in 4-year disease-free survival (DSS), progression-free survival (DFS) and overall survival (OS) were 0.87, 0.79 and 0.79 respectively.ConclusionAt present, radiomics has a valuable impact on preoperative staging, neoadjuvant therapy evaluation, and survival analysis of colorectal cancer.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.