Individualized Treatment Plans Research Articles

Drug landscape in patients receiving general outpatient palliative care in Germany: results from a retrospective analysis of 10,464 patients

BackgroundAccording to § 27 and § 87 1b of the German Social Code, Book V, general outpatient palliative care (GOPC) aims to promote, maintain, and improve the quality of life and self-determination of seriously ill people. It should enable them to live in dignity until death in their preferred environment. Instead of a curative approach GOPC treatment focuses on the multiprofessional objective of alleviating symptoms and suffering on a case-by-case basis using medication or other measures, as well as the management of an individual treatment plan. The aim of this study was therefore to investigate to what extent medication differs from 12 months prior GOPC treatment within 12 months following GOPC treatment.MethodsA retrospective database cross sectional study based on the IQVIA Disease Analyzer (DA) was performed, including adult patients with cancer diagnosis and at least one documentation of palliative support between January 1st, 2018 and December 31st, 2021, in 805 general practices (GP).ResultsThe results of this study show, that in the context of general general outpatient palliative care, there is a significant increase in the prescription of opioids (18.3% vs. 37.7%), sedatives (7.8% vs. 16.2%) and antiemetics (5.3% vs. 9.7%), as well as a significant reduction in other medications such as statins (21.4% vs. 11.5%), proton pump inhibitors (PPI) (41.2% vs. 35.3%), or antihypertensives (57.5% vs. 46.6%).ConclusionsOur results support the role of GOPC as an important element in improving pharmacological symptom control and deprescription to improve quality of life of patients at the end of their life.

Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Treatment of actinic keratoses in older adults

Actinic keratoses (AKs) are common precancerous skin lesions that primarily affect older adults due to cumulative sun exposure. Given the increased vulnerability of older adults to developing AKs, appropriate therapeutic strategies are crucial to prevent their progression to invasive squamous cell carcinoma. This comprehensive review aims to explore the various treatment modalities available for AKs in the elderly population, focusing on their efficacy, safety, and suitability for this specific age group. The article discusses topical treatments, cryotherapy, photodynamic therapy, chemical peels, and surgical interventions, providing adetailed analysis of their mechanisms of action, benefits, limitations, and considerations in geriatric patients. Furthermore, the importance of individualized treatment plans, considering factors such as comorbidities, medication interactions, and patient preferences, are highlighted.

Generalized information with examples on the possibility of using a service-oriented approach and artificial intelligence technologies in the industry of e-Health

The object of the research is the review of ways of implementing service-oriented approaches (SOA) and artificial intelligence (AI) technologies in modern healthcare systems. The generalization of these ways will allow to cope with complex modern challenges, such as increasing demand for medical services, growing volumes of data, and the need for high-quality and effective treatment. This work is aimed at this. The field of e-Health is rapidly gaining popularity and combines many different systems. But due to the large number of tools and system providers with different architectures, there is a problem that different systems are difficult or impossible to integrate and connect with each other. It is shown that the use of SOA makes it possible to break down complex systems into separate services that can interact with each other to ensure fast and accurate data processing, effective management of medical resources, and improvement of the quality of medical services. AI can be used to analyze large volumes of medical data, predict risks, diagnose diseases, and develop individualized treatment plans. The use of AI in healthcare systems helps improve diagnostic accuracy, reduce treatment times, and improve patient outcomes. The synergy of SOA and AI in health care systems is important when SOA provides the means to integrate various AI solutions, which allows for the interaction of different services and the exchange of data to ensure effective treatment and collaboration between medical professionals and artificial intelligence systems. Such distribution of systems makes it possible to scale them without affecting other services that are already running. Therefore, it becomes possible to use unified data transfer protocols and combine different services into one system without radically changing the codebase and building additional layers of abstraction for interaction between services that cannot be combined in one system. Examples of the use of SOA and AI in modern health care systems to improve the quality of medical services, optimize resources and ensure an individual and effective approach to patient treatment, which can be used at the next stages of medical reform in Ukraine, are considered.

Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders.

Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts have been limited in their ability to identify individual-level models of eating disorder psychopathology and to develop and apply an individually tailored treatment for a given individual's personalized model of psychopathology. In addition, research is still needed to identify causal relationships within a given individual's model of eating disorder psychopathology. Addressing this limitation of the current state of precision medicine-related research in the field will allow us to progress toward advancing research and practice for eating disorders treatment. We present a novel set of analytic tools, causal discovery analysis (CDA) methods, which can facilitate increasingly fine-grained, person-specific models of causal relations among cognitive, behavioral, and affective symptoms. CDA can advance the identification of an individual's causal model that maintains that individuals' eating disorder psychopathology. In the current article, we (1) introduce CDA methods as a set of promising analytic tools for developing precision medicine methods for eating disorders including the potential strengths and weaknesses of CDA, (2) provide recommendations for future studies utilizing this approach, and (3) outline the potential clinical implications of using CDA to generate personalized models of eating disorder psychopathology. CDA provides a novel statistical approach for identifying causal relationships among variables of interest for a given individual. Person-specific causal models may offer a promising approach to individualized treatment planning and inform future personalized treatment development efforts for eating disorders.

Integration of Person-Centered Narratives Into the Electronic Health Record.

Care delivery that is not person-centered has been called discordant care . There has been a shift to incorporate more of a person's narrative into their individual healthcare treatment plan to reduce discordant care. Aligning with this shift in healthcare delivery, we developed a person-centered narrative intervention (PCNI) to address existing gaps in delivery of person-centered care. This study aimed to evaluate the feasibility of conducting a randomized study and describe the outcomes of PCNI to usual care on the following person (patient)-reported outcomes: perceptions of the quality of communication with their nurses and their psychosocial and existential well-being. This study's design was an Obesity-Related Behavioral Intervention Trials model Phase II proof-of-concept randomized study. The participants were people admitted to an acute care hospital diagnosed with heart failure and/or end-stage renal disease. Despite COVID-19 challenges, the PCNI was feasible in an acute care setting; it showed a moderate positive difference between conditions in the person's perception of their quality of communication and a small positive difference in their perception of feeling heard and understood. For our secondary outcomes of anxiety, depression, and psychosocial illness effect, there were small or no effects in the acute care setting. Using a person-centered narrative, such as the PCNI, can help inform delivery of care that incorporates a person's (patient's) beliefs, values, and preferences into their healthcare. This study used a pragmatic approach to evaluate the PCNI in real time in an acute care setting to assess patient-reported outcomes. These positive results in a small sample indicate the need for continued testing of the PCNI. These promising effects require further testing in a Phase III efficacy study within a larger randomized controlled clinical trial.

Identification and Characterization of Pain Processing Patterns Among Patients with Chronic Primary Pain: A replication.

To develop individual and effective treatment plans for patients with chronic pain, we aimed to replicate Grolimund et al's (2017) empirical categorization of chronic pain patients on a new and larger sample. Moreover, this work aimed to extend previous knowledge by considering various treatment outcomes and exploratorily analyzing which coping skills might be particularly relevant for treatment success in each subtype. Latent class analysis was used to identify homogenous subtypes with different pain processing patterns using the pain processing questionnaire (FESV). By analyzing 602 inpatients with chronic primary pain, we identified three subtypes: (1) severely burdened individuals with low coping skills, (2) mildly burdened individuals with high coping skills, and (3) moderately burdened individuals with moderate coping skills. Pain interference, psychological distress, cognitive and behavioral coping skills improved after treatment in all subtypes. Pain-related mental interference significantly improved only in subtypes (1) and (3). Only individuals of subtype (3) reported significant reductions in pain intensity after treatment. Exploratory regression analysis suggested that of subtype (1), the most promising targets in reducing pain interference and psychological distress post-treatment might be to foster relaxation techniques, counteractive activities, and cognitive restructuring. None of the FESV dimensions significantly predicted treatment outcomes among individuals of subtype (2). Individuals of subtype (3) might benefit the most from experiencing more competence during treatment. Our findings highlight the importance of identifying and characterizing subtypes of chronic primary pain patients and that these subtypes should be considered for individualized and effective treatment.

AYURVEDIC APPROACH TO GUILLAIN-BARRÉ SYNDROME: RESTORING BALANCE AND STRENGTHENING THE NERVOUS SYSTEM

Guillain-Barré Syndrome (GBS) is a neurological disorder characterized by muscle weakness and paralysis caused by the immune system attacking the peripheral nerves. While conventional medical treatments are essential for managing GBS, Ayurveda, the ancient Indian system of medicine, offers holistic approaches to complement conventional therapies. This article explores Ayurvedic perspectives on GBS and presents a range of Ayurvedic treatment strategies, including Panchakarma therapy, herbal medicines, diet and lifestyle modifications, Ayurvedic medicinal oils, and stress management techniques. These approaches aim to restore balance to the Vata dosha, rejuvenate the nervous system, and support overall well-being. While Ayurveda does not claim to provide a cure for GBS, integrating Ayurvedic principles with conventional treatments can potentially enhance recovery and improve symptom management. A collaborative approach between Ayurvedic practitioners and medical professionals is crucial for developing personalized and effective treatment plans for individuals with GBS.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives.

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Norwegian Version of the Chelsea Critical Care Physical Assessment Tool (CPAx-NOR): Translation, Face Validity, Cross-Cultural Adaptation and Inter-Rater Reliability.

Assessment of physical and respiratory function in the intensive care unit (ICU) is useful for developing an individualized treatment plan and evaluating patient progress. There is a need for measurement tools that are culturally adapted, reliable and easy to use. The Chelsea Critical Care Physical Assessment Tool (CPAx) is a valid measurement tool with strong psychometric properties for the intensive care population. This study aims to translate, adapt and test face validity and inter-rater reliability of the Norwegian version of CPAx (CPAx-NOR) for use in critically ill adult patients receiving prolonged mechanical ventilation. CPAx-NOR was forward backward translated, culturally adapted and tested by experts and patients for face validity. Thereafter tested by 10 physiotherapists in five hospitals for inter-rater reliability. The experts and pilot testers reached consensus on the translation and face validity. Patients were tested at time point A (n = 57) and at time point B (n = 53). The reliability of CPAx-NOR at "A" was 0.990 (0.983-0.994) and at "B" 0.994 (0.990-0.997). Based on A+B combined and adjusted, the ICC was 0.990 (95% CI 0.996-0.998). Standard error of measurement (SEM) was 0.68 and the minimal detectable change (MDC) was 1.89. The Bland-Altman plot showed low bias and no sign of heteroscedasticity. CPAx-NOR changed with a mean score of 14.9, and showed a moderate floor effect at the start of physiotherapy and low ceiling effects at discharge. CPAx-NOR demonstrated good face validity and excellent inter-rater reliability. It can be used as an assessment tool for physical function in critically ill adults receiving prolonged mechanical ventilation in Norway.

Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, and Cox-maze IV procedure (CMP-IV) is a commonly employed surgical technique for its treatment. Currently, the risk factors for atrial fibrillation recurrence following CMP-IV remain relatively unclear. In recent years, machine learning algorithms have demonstrated immense potential in enhancing diagnostic accuracy, predicting patient outcomes, and devising personalized treatment strategies. This study aims to evaluate the efficacy of CMP-IV on treating chronic valvular disease with AF, utilize machine learning algorithms to identify potential risk factors for AF recurrence, construct a CMP-IV postoperative AF recurrence prediction model. A total of 555 patients with AF combined with chronic valvular disease, who met the criteria, were enrolled from January 2012 to December 2019 from the Second Xiangya Hospital of Central South University and the Affiliated Xinqiao Hospital of the Army Medical University, with an average age of (57.95±7.96) years, including an AF recurrence group (n=117) and an AF non-recurrence group (n=438). Kaplan-Meier method was used to analyze the sinus rhythm maintenance rate, and 9 machine learning models were developed including random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), bootstrap aggregating, logistic regression, categorical boosting (CatBoost), support vector machine, adaptive boosting, and multi-layer perceptron. Five-fold cross-validation and model evaluation indicators [including F1 score, accuracy, precision, recall, and area under the curve (AUC)] were used to evaluate the performance of the models. The 2 best-performing models were selected for further analyze, including feature importance evaluation and Shapley additive explanations (SHAP) analysis, identifying AF recurrence risk factors, and building an AF recurrence risk prediction model. The 5-year sinus rhythm maintenance rate for the patients was 82.13% (95% CI 78.51% to 85.93%). Among the 9 machine learning models, XGBoost and CatBoost models performed best, with the AUC of 0.768 (95% CI 0.742 to 0.786) and 0.762 (95% CI 0.723 to 0.801), respectively. Feature importance and SHAP analysis showed that duration of AF, preoperative left ventricular ejection fraction, postoperative heart rhythm, preoperative neutrophil-to-lymphocyte ratio, preoperative left atrial diameter, preoperative heart rate, and preoperative white blood cell were important factors for AF recurrence. Conclusion: Machine learning algorithms can be effectively used to identify potential risk factors for AF recurrence after CMP-IV. This study successfuly constructs 2 prediction model which may enhance individualized treatment plans.

Selective Accumulation of Near Infrared-Labeled Multivalent Quinidine Copolymers in Tumors Overexpressing P-Glycoprotein: Potential for Noninvasive Diagnostic Imaging.

P-glycoprotein (P-gp) is a promiscuous small molecule transporter whose overexpression in cancer is associated with multidrug resistance (MDR). In these instances, anticancer drugs can select for P-gp-overexpressing cells, leading to cancer recurrence with an MDR phenotype. To avoid selection for MDR cancers and inform individual patient treatment plans, it is critical to noninvasively identify P-gp-overexpressing tumors prior to administration of chemotherapy. We report the facile free radical copolymerization of quinidine, a competitive inhibitor of P-gp, and acrylic acid to generate multiplexed polymeric P-gp-targeted imaging agents with tunable quinidine content. Copolymer targeting was demonstrated in a nude mouse xenograft model. In xenografts overexpressing P-gp, copolymer distribution was enhanced over two-fold compared to the negative control of poly(acrylic acid) regardless of quinidine content. In contrast, accumulation of the copolymers in xenografts lacking P-gp was equivalent to poly(acrylic acid). This work forms the foundation for a unique approach toward the phenotype-specific noninvasive imaging of MDR tumors and is the first in vivo demonstration of copolymer accumulation through the active targeting of P-gp.

Outcomes of children treated for multiple Epstein-Barr virus-associated post-transplant tumors.

After solid organ transplantation, children are at risk for Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. Little is known about the clinical course, Epstein-Barr viral load variations, and optimal treatment for such patients. We set forth to understand the course of repeated episodes of post-transplant lymphoproliferative disorder and smooth muscle tumors. We performed a retrospective chart review of patients up to 21 years old with solid organ transplantation and post-transplant lymphoproliferative disorder at the Children's Hospital of Philadelphia from January 2003 through June 30, 2020. Six patients had multiple episodes of Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. When the second episode was discovered, only one patient was symptomatic. Histology differed from diagnosis in four patients. Treatment included viral-specific T-lymphocytes (2), rituximab (3), reduction in immunosuppression alone (1). Five patients had complete response, and one had stable disease, but three patients developed a subsequent tumor. Two patients developed Epstein-Barr virus-associated smooth muscle tumors. Of these six patients, four are alive. The deaths were not related to their tumors. Despite a complete response to initial therapy, children are at risk for repeated episodes of Epstein-Barr virus-associated post-transplant lymphoproliferative disorder and smooth muscle tumors. Histology and location were not typically consistent with initial diagnosis, suggesting these are second primaries rather than recurrences. Disease may be managed with individualized treatment plans but EBV-specific T cells need further study in such tumors.

Vision-Guided Attention-Enhanced Network for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

As the most common malignant tumor worldwide, hepatocellular carcinoma (HCC) has a high rate of death and recurrence, and microvascular invasion (MVI) is considered to be an independent risk factor affecting its early recurrence and poor survival rate. Accurate preoperative prediction of MVI is of great significance for the formulation of individualized treatment plans and long-term prognosis assessment for HCC patients. However, as the mechanism of MVI is still unclear, existing studies use deep learning methods to directly train CT or MR images, with limited predictive performance and lack of explanation. We map the pathological "7-point" baseline sampling method used to confirm the diagnosis of MVI onto MR images, propose a vision-guided attention-enhanced network to improve the prediction performance of MVI, and validate the prediction on the corresponding pathological images reliability of the results. Specifically, we design a learnable online class activation map (CAM) to guide the network to focus on high-incidence regions of MVI guided by an extended tumor mask. Further, an attention-enhanced module is proposed to force the network to learn image regions that can explain the MVI results. The generated attention maps capture long-distance dependencies and can be used as spatial priors for MVI to promote the learning of vision-guided module. The experimental results on the constructed multi-center dataset show that the proposed algorithm achieves the state-of-the-art compared to other models.

Contact Force-Guided versus Contact Force-Blinded Cavo-Tricuspid Isthmus Ablation for Atrial Flutter: A Systematic Review and Meta-Analysis.

Contact force (CF) is a novel approach developed to increase the safety and efficacy of catheter ablation. However, the value of CF-sensing technology for atrial flutter (AFL) cavo-tricuspid isthmus ablation (CTIA) is inconclusive. To generate a comprehensive assessment of optimal extant data on CF for AFL, we synthesized randomized controlled trials (RCTs) and observational studies from Web of Science, SCOPUS, EMBASE, PubMed, and Cochrane until 29 November 2022, using the odds ratio (OR) for dichotomous outcomes and mean difference (MD) for continuous outcomes with a corresponding 95% confidence interval (CI). Two RCTs and three observational studies with a total of 376 patients were included in our analysis. CF-guided ablation was associated with (A) a higher rate of AFL recurrence (OR: 2.26 with 95% CI [1.05, 4.87]) and total CF (MD: 2.71 with 95% CI [1.28, 4.13]); (B) no effect on total procedure duration (MD: -2.88 with 95% CI [-7.48, 1.72]), fluoroscopy duration (MD: -0.96 with 95% CI [-2.24, 0.31]), and bidirectional isthmus block (BDIB) (OR: 1.50 with 95% CI [0.72, 3.11]); and (C) decreased radiofrequency (RF) duration (MD: -1.40 with 95% CI [-2.39, -0.41]). We conclude that although CF-guided CTIA was associated with increased AFL recurrence and total CF and reduced RF duration, it did not affect total procedure duration, fluoroscopy duration, or BDIB. Thus, CF-guided CTIA may not be the optimal intervention for AFL. These findings indicate the need for (A) providers to balance the benefits and risks of CF when utilizing precision medicine to develop treatment plans for individuals with AFL and (B) clinical trials investigating CF-guided catheter ablation for AFL to provide definitive evidence of optimal CF-sensing technology.

Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images.

The aim of this study is to apply deep learning techniques for the development and validation of a system that categorizes various phases of dry age-related macular degeneration (AMD), including nascent geographic atrophy (nGA), through the analysis of optical coherence tomography (OCT) images. A total of 3,401 OCT macular images obtained from 338 patients admitted to Shenyang Aier Eye Hospital in 2019-2021 were collected for the development of the classification model. We adopted a convolutional neural network (CNN) model and introduced hierarchical structure along with image enhancement techniques to train a two-step CNN model to detect and classify normal and three phases of dry AMD: atrophy-associated drusen regression, nGA, and geographic atrophy (GA). Five-fold cross-validation was used to evaluate the performance of the multi-label classification model. Experimental results obtained from five-fold cross-validation with different dry AMD classification models show that the proposed two-step hierarchical model with image enhancement achieves the best classification performance, with a f1-score of 91.32% and a kappa coefficients of 96.09% compared to the state-of-the-art models. The results obtained from the ablation study demonstrate that the proposed method not only improves accuracy across all categories in comparison to a traditional flat CNN model, but also substantially enhances the classification performance of nGA, with an improvement from 66.79 to 81.65%. This study introduces a novel two-step hierarchical deep learning approach in categorizing dry AMD progression phases, and demonstrates its efficacy. The high classification performance suggests its potential for guiding individualized treatment plans for patients with macular degeneration.

The Role of Epigenetic Modifications in Drug Response and Resistance

The inability of epigenetic modifications to control gene expression has been related to a variety of diseases and conditions, including cancer and neurological ailments, amongst others. In recent years, there has been a rise in the amount of attention in pharmacology that is focused on the role that epigenetic modifications play in the response to medicine and the development of drug resistance. This concise review will attempt to provide a synopsis of what is currently known on the influence that epigenetic changes have on the treatment efficacy and resistance mechanisms of various diseases. DNA methylation, histone modifications, and non-coding RNAs are some of the key epigenetic markers that are covered in this article in relation to their function in the pharmacokinetics and pharmacodynamics of drugs. In addition, we highlight the development of approaches that attempt to enhance treatment outcomes while simultaneously overcoming drug resistance by targeting epigenetic alterations. A fundamental understanding of the connection between epigenetics and pharmacological response is required for the development of individualized treatment plans and the identification of novel therapeutic targets.

Injection anatomy-a new discipline in aesthetic medicine

The basic anatomical understanding of aesthetic medicine has changed fundamentally since the turn of the millennium. With the advent of modern minimally invasive injection procedures using botulinum toxin and hyaluronic acid fillers, anatomical structures that had previously received little attention and were not considered relevant have steadily gained in importance and moved into the scientific focus. Understanding the three-dimensional arrangement of the face with its defined anatomical layers and compartments serves the procedural dermatologist in the sense of anavigation system to optimize treatment success and patient safety when performing aesthetic injections in the facial region. The principles of the biomechanical interaction of the structures are of elementary importance in the creation of individual treatment plans for the implementation of natural and balanced therapeutic results. Modern injection anatomy as an interdisciplinary subject of the three fields of anatomy, dermatology and plastic surgery makes adecisive contribution here.

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

ObjectiveTo investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.MethodsA retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.ResultsGender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).ConclusionThe R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.Critical relevance statementThe radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.Graphical abstract

Research progress of different minimally invasive spinal decompression in lumbar spinal stenosis

To review the application and progress of different minimally invasive spinal decompression in the treatment of lumbar spinal stenosis (LSS). The domestic and foreign literature on the application of different minimally invasive spinal decompression in the treatment of LSS was extensively reviewed, and the advantages, disadvantages, and complications of different surgical methods were summarized. At present, minimally invasive spinal decompression mainly includes microscopic bilateral decompression, microendoscopic decompression, percutaneous endoscopic lumbar decompression, unilateral biportal endoscopy, and so on. Compared with traditional open surgery, different minimally invasive spinal decompression techniques can reduce the operation time, intraoperative blood loss, and postoperative pain of patients, thereby reducing hospital stay and saving treatment costs. The indications of different minimally invasive spinal decompression are different, but there are certain advantages and disadvantages. When patients have clear surgical indications, individualized treatment plans should be formulated according to the symptoms and signs of patients, combined with imaging manifestations.