Customized Treatment Strategies Research Articles

The complex role of immune cells in antigen presentation and regulation of T-cell responses in hepatocellular carcinoma: progress, challenges, and future directions

Hepatocellular carcinoma (HCC) is a prevalent form of liver cancer that poses significant challenges regarding morbidity and mortality rates. In the context of HCC, immune cells play a vital role, especially concerning the presentation of antigens. This review explores the intricate interactions among immune cells within HCC, focusing on their functions in antigen presentation and the modulation of T-cell responses. We begin by summarizing the strategies that HCC uses to escape immune recognition, emphasizing the delicate equilibrium between immune surveillance and evasion. Next, we investigate the specific functions of various types of immune cells, including dendritic cells, natural killer (NK) cells, and CD8+ T cells, in the process of antigen presentation. We also examine the impact of immune checkpoints, such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and the pathways involving programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1), on antigen presentation, while taking into account the clinical significance of checkpoint inhibitors. The review further emphasizes the importance of immune-based therapies, including cancer vaccines and CAR-T cell therapy, in improving antigen presentation. In conclusion, we encapsulate the latest advancements in research, propose future avenues for exploration, and stress the importance of innovative technologies and customized treatment strategies. By thoroughly analyzing the interactions of immune cells throughout the antigen presentation process in HCC, this review provides an up-to-date perspective on the field, setting the stage for new therapeutic approaches.

Closed traumatism of the distal pancreas (A case series of 6 patients)

Introduction and importanceThe aim of this retrospective study was to present six cases of trauma to the distal pancreas, highlighting the challenges associated with their diagnosis and management, while underlining their seriousness and the various complications potentially encountered. Our case series highlights individual patient outcomes, demonstrating the diversity of clinical presentations and the importance of customized treatment strategies. Case seriesBetween January 2015 and December 2020, six cases of distal pancreas trauma were identified. In two cases, the diagnosis was made based on emergency abdominal CT scans, while in the other four patients, the diagnosis was made directly intraoperatively, mainly because of the severity of the associated lesions, which necessitated laparotomy for exploration. Clinical discussionOut of 115 cases of closed abdominal trauma, injury to the distal pancreas was identified in 6 patients, (5.2 %), with a mean age of 21 years. Despite the use of abdominal CT scans for all patients, pancreatic trauma was directly diagnosed intraoperatively in 4 cases (67 %). All patients presented with concomitant abdominal injuries (100 %), and 3 patients (50 %) exhibited multiple severe injuries. Additionally, a significant elevation in pancreatic serum markers was observed in 3 patients (50 %). The pancreatic injuries predominantly involved the tail of the pancreas (67 %), while the body was affected in one patient, and the isthmus was completely transected in another.Three of our patients developed a pancreatic fistula (50 %) and two patients (33 %) passed away; the first had severe associated lesions, and the second, despite undergoing several iterative laparotomies, succumbed to postoperative complications following a left pancreatectomy. ConclusionClosed traumatism of the distal pancreas, although rare, is a significant problem. It is often diagnosed during emergency laparotomy but can sometimes be found on preoperative CT scans. When the patient's condition permits, it is highly advisable to undergo a left pancreatectomy. Simple external drainage is reserved for certain specific situations.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Fracture Non-Union in Osteoporotic Bones: Current Practice and Future Directions.

Given the compromised bone quality and altered healing environment, fracture non-union in osteoporotic bones presents a complex challenge in orthopedics. As global populations age, the incidence of osteoporotic fractures rises, leading to increased delayed healing and non-union cases. The pathophysiology underlying non-union in osteoporotic patients involves impaired bone regeneration, reduced osteoblast function, and poor vascularity. Traditional management strategies - ranging from pharmacological interventions like bisphosphonates and teriparatides to surgical approaches such as bone grafting and mechanical fixation - often yield limited success due to the weakened bone structure. Recent advances, however, have introduced novel therapies such as growth factors, stem cell applications, gene therapy, and bioactive scaffolds that offer more targeted and biologically driven solutions. Emerging technologies like three-dimensional printing and nanotechnology further contribute to customized treatment strategies that hold promise for improved outcomes. Diagnostic approaches have also evolved, integrating radiological assessments and biomarkers to identify patients at risk for non-union better. Despite these advancements, challenges remain, including the high costs, technical complexities, and the need for more robust clinical evidence. Future directions involve optimizing these innovative treatments, validating their effectiveness in more extensive clinical trials, and integrating personalized medicine approaches to cater to the individual needs of osteoporotic patients. Overall, integrating these emerging therapeutic strategies alongside traditional practices represents a significant shift towards more effective and personalized management of fracture non-union in osteoporotic bones.

Prevalence and antimicrobial resistance of bacterial meningitis in China from 2017 to 2021: a multicenter retrospective study.

This study aims to investigate the changing epidemiology and antimicrobial susceptibility of bacteria isolated from cerebrospinal fluid (CSF) in the Shandong region. We conducted a retrospective analysis of bacterial distribution and resistance patterns in CSF samples, utilizing data from the SPARSS network and analyzed with WHONET 5.6 software. A total of 3968 pathogenic bacterial strains were isolated, consisting of 70.6% Gram-positive bacteria, 27.2% Gram-negative bacteria, and 0.2% fungi. The six most commonly detected bacteria were coagulase-negative staphylococcus, Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Escherichia coli, and staphylococcus aureus. Analysis revealed gender and seasonal variations in the distribution of CSF pathogens, with a higher incidence observed in males and during autumn compared to other seasons. The susceptibility profiles of these bacterial species varied significantly, with many exhibiting multidrug resistances. A. baumannii showed a high resistance rate to cephalosporins and carbapenems but was sensitive to tigecycline and polymyxins. For treating multidrug-resistant A. baumannii infections, polymyxin-based combinations with tigecycline or sulbactam are recommended for adults, while tigecycline combined with meropenem is suggested for children. Enterobacteriaceae species were generally sensitive to carbapenems, such as meropenem and other carbapenems that can penetrate the blood-brain barrier can be recommended. Linezolid and vancomycin are the first choice for treating common gram-positive bacterial infections. The high resistance rates observed among common CSF isolates and their varied distributions across different demographics highlight the necessity for customized treatment strategies.

Retrospective Analysis of Treatment Outcomes and Survival Rates in Oral Squamous Cell Carcinoma Patients

ABSTRACT Background: Because of its high rates of morbidity and death, oral squamous cell carcinoma (OSCC) poses a serious threat to world health. To maximize patient care and direct clinical decision-making, it is important to comprehend treatment results and survival rates. Methods: Treatment results and survival rates for OSCC patients treated at a tertiary care facility between 2010 and 2022 were assessed in this retrospective research. Demographic data, tumor features, treatment options, and follow-up results were all included in the data. The relationships between treatment methods and survival rates were evaluated by statistical analysis. Results: There were 107 OSCC patients in all. Compared to radiation and chemotherapy, surgery had reduced recurrence rates and greater overall survival rates as the most common treatment option. There were notable variations in survival rates across the various treatment regimens (P < 0.05). Conclusion: Surgery is the mainstay. Adjuvant treatments, however, are complementary in the management of illness. The significance of customized treatment strategies based on unique patient attributes and tumor features is highlighted by this study in order to maximize results for patients with OSCC. To confirm these results and investigate cutting-edge treatment approaches, more investigation is necessary.

A Comprehensive Review on AI-Driven Healthcare Transformation

Introduction: In the dynamic landscape of healthcare, the intersection of cutting-edge technology and patient-centric solutions has sparked a paradigm shift, and at the forefront of this transformative wave is Artificial Intelligence (AI). Problem: Focusing on the multifaceted integration of AI technologies, the narrative explores their pivotal role in enhancing patient outcomes. Objective: This paper delves into the dynamic realm of healthcare transformation over the strategic exploitation of artificial intelligence (AI). Methodology: This paper explores the profound impact of AI on healthcare, examining the current state of the field and envisioning its future. From machine learning algorithms for early disease detection to personalized treatment plans, the paper delves into the diverse applications of AI in healthcare and the potential it holds for revolutionizing the entire ecosystem. From customized treatment strategies to streamlined healthcare processes, the paper unravels the diverse ways AI is reshaping the healthcare landscape. Results: The imperative to improve patient outcomes, characterized by enhanced efficiency, precision, and personalized care, finds a promising ally in AI. The paper has illuminated how AI technologies, including machine learning and advanced analytics, offer tangible solutions to longstanding challenges in diagnostics, treatment planning, and patient engagement. Conclusions: A comprehensive examination of emerging trends illuminates the potential for substantial improvements in patient well-being facilitated by the synergistic partnership of AI and healthcare practices. Originality: The exploration of AI’s applications throughout this paper underscores its transformative potential in reshaping traditional healthcare practices. Restrictions: The prospect of continually improving patient outcomes through innovative technologies remains at the forefront of this transformation.

A Comprehensive Study on the Role of AI for Next-Generation Healthcare

Artificial Intelligence (AI) is transforming the healthcare landscape through the utilization of data analytics, automation, and machine learning methods to innovate patient care, operational effectiveness, and medical research. This paper delves into the complicated impact of AI in healthcare, emphasizing its influence on precision medicine, medical imaging, drug discovery, healthcare operations, and patient engagement. Within precision medicine, AI examines a variety of patient data, such as genetic details and medical backgrounds, to customize treatment strategies and enhance diagnostic precision. AI-driven medical imaging algorithms assist in the timely identification and diagnosis of illnesses like cancer and cardiovascular diseases, ultimately enhancing patient outcomes. Additionally, AI accelerates the drug discovery process by inspecting molecular configurations, forecasting drug interactions, and pinpointing potential candidates for clinical trials, thereby expediting therapeutic advancements. AI-powered solutions streamline healthcare operations by automating administrative duties, resource distribution, and workflow supervision, leading to enhanced efficiency and cost reduction. Remote patient monitoring via AI-enabled gadgets and wearables allows for continuous health monitoring, facilitating proactive management of chronic ailments and early diagnosis. The integration of Natural Language Processing (NLP) enables AI virtual assistants to engage with patients, arrange appointments, and extract insights from unstructured data such as electronic health records. Nevertheless, ethical and regulatory considerations, encompassing data privacy and bias mitigation, are critical in the integration of AI to ensure patient safety and trust.

Clinical and Imaging Biomarkers Associated with Outer Retinal Atrophy Onset in Exudative Age-Related Macular Degeneration: A Real-Word Prospective Study.

Macular neovascularization (MNV) secondary to age-related macular degeneration (AMD) is well managed by anti-vascular endothelial growth factor (anti-VEGF) intravitreal injections. However, outer retinal atrophy represents an unavoidable occurrence detected during follow-up. Several imaging metrics have been proposed as clinically relevant in stratifying the risk of onset of outer retinal atrophy. The main goal of this study is to evaluate the impact of noninvasive imaging metrics on the assessment of outer retinal atrophy onset in a large cohort of eyes with neovascular AMD managed in a real-world setting. This study was a prospective, observational, case series. We included patients affected by newly diagnosed neovascular AMD, requiring anti-VEGF intravitreal injections. We collected clinical and imaging data, with a planned follow-up of 24months. The multimodal imaging protocol included optical coherence tomography, optical coherence tomography angiography, and fundus autofluorescence. We collected noninvasive imaging metrics and we assessed the relationship with the morphological and functional outcome evaluated at 12-month and 24-month time points. We included 370 eyes of 370 patients with exudative AMD (210 male; mean age 79 ± 8years). MNV were classified as follows: type 1, 198 (54%); type 2, 89 (24%); polypoidal choroidal vasculopathy, 29 (7%); and type 3, 54 (15%). A total of 120 out of 370 eyes (33%) showed complete outer retinal atrophy at the end of the 2-year follow-up. The presence of intraretinal fluid, thinning of the Sattler choroidal layer, late anti-VEGF switch, the overall number of anti-VEGF injections, and the perfusion characteristics of the MNV were found to be the most relevant factors associated with the onset of outer retinal atrophy. The other collected metrics were found to be less clinically relevant, also showing no cumulative effect in the multivariate analysis (p > 0.05). We identified imaging metrics significantly associated with the 2-year risk onset of outer retinal atrophy. These metrics might pave the way for the development of future customized anti-VEGF treatment strategies.

Association of Body Mass Index and Chronology of Tooth Eruption in Children visiting a Dental Hospital in UAE: A Cross-sectional Study

AimThe objective of this research was to investigate the relationship between body mass index (BMI) and the mean age at which permanent teeth erupt in school-going children. Materials and Method: This cross-sectional study at RAK College of Dental Sciences, UAE, involved 89 children aged 6–14 years. Erupted teeth in the mouth were included. Statistical analysis, including t-tests, ANOVA, and Kruskal-Wallis, was conducted using SPSS version 29, with a significance threshold set at p < 0.05. Results: Out of 89 children (41 girls and 48 boys) in the study, 56.17 % had normal weight, 20.22 % were overweight, 17.97 % were underweight, and 5.6 % were obese. Female children generally experienced earlier permanent tooth eruption than males. Additionally, there was an observed trend of earlier tooth eruption with increasing weight or BMI. Underweight children notably displayed a delayed mean age of tooth eruption. Conclusion: This study demonstrates a notable correlation between BMI and the mean age at which permanent teeth erupt in school-going children aged 6 to 14 years who visited our dental hospital. To establish a more precise understanding of the connection between BMI and dental development, we recommend conducting further longitudinal studies involving multiple centers. Clinical Significance: Monitoring a child's BMI is crucial for assessing dental health and planning tailored treatment for those aged 6 to 14. Understanding the BMI's connection to permanent tooth eruption timing allows dental professionals to identify trends in early or delayed development. This enables them to customize treatment strategies, promoting a precise and personalized approach for better oral health outcomes in this age group.

Deep learning applications in MRI for brain tumor detection and image segmentation

Deep learning holds great potential in the field of MRI applications. By leveraging its advanced algorithms and neural networks, it can effectively analyze and interpret intricate patterns in medical images, aiding in precise disease detection, segmentation, and classification. Integrating deep learning techniques with MRI technology is expected to revolutionize radiology practice, facilitating enhanced diagnostic accuracy and customized treatment strategies, ultimately leading to improved patient outcomes. This article provides an overview of of the latest advancements in deep learning techniques applied to magnetic resonance imaging, specifically focusing on brain tumor detection and segmentation. The study examines eight different deep learning methods, including a multi-scale convolutional neural network, U-Net-based fully convolutional networks, cascaded anisotropic convolutional neural networks, missing modality-based tumor segmentation, Hough-CNN for deep brain region segmentation, k-Space deep learning for accelerated MRI, Multi-level Kronecker Convolutional Neural Network, and a heuristic approach for clinical brain tumor segmentation. Each method is analyzed, highlighting its specific techniques, advantages, and limitations. The comparative performance of these methods in terms of accuracy and efficiency, addressing key factors such as computational requirements, training time, and robustness, was discussed in this article. By assessing the merits and limitations of different approaches, this review seeks to offer valuable perspectives on effective utilization of deep learning techniques in clinical MRI settings for detecting and delineating brain tumors.

Presentation, Radiologic Features, and Treatment Options of Congenital Tongue Tumors: A Comprehensive Review.

Congenital tumors of the tongue are rare in pediatric patients but encompass a diverse range of entities. Each tumor type exhibits distinct clinical behaviors, necessitating a precise approach to differentiating the tumor types and a tailored, tumor-specific treatment regimen. Advanced imaging techniques, such as diffusion-weighted imaging and perfusion studies, play a vital role in differentiating benign and malignant tongue tumors. This review summarizes current knowledge regarding the presentation, imaging features, and treatment of congenital tongue tumors. A literature review was conducted by searching studies on congenital tongue tumors in databases such as PubMed, Embase, Web of Science, and Scopus. Relevant data, such as clinical features, radiologic characteristics, treatment modalities, and outcomes for different tumor types, were extracted from the selected articles. Our literature review reveals the various entities of congenital tongue tumors, which can be categorized in terms of hereditary pattern, phenotype, and rarity. Congenital tongue tumors include a range of vascular malformations, such as hemangiomas, lymphatic malformations, arteriovenous malformations, and venous malformations. Another entity is represented by cystic lesions, including dermoid cysts, epidermoid cysts, ranulas, and mucous retention cysts. Rare malignant neoplasms include teratomas and rhabdomyosarcomas. These tumor types vary in terms of swelling, respiratory distress, or impaired oral function, depending on size and location. The detection of these tumors can be carried out using imaging modalities, such as ultrasound, magnetic resonance imaging, and computed tomography, which are utilized to facilitate diagnosis and differentiation. At present, surgical excision remains the cornerstone of treatment, while other modalities may be adopted, depending on tumor type and extent. The prognosis of congenital tongue tumors can be affected by tumor's site, size, involvement of vital structures, and malignancy. Given their diversity and complexity, congenital tongue tumors, albeit uncommon, require specialized clinical treatments tailored to each tumor type's characteristics. Understanding the variable presentations and imaging features enables accurate diagnosis, while customized treatment strategies are key to optimizing outcomes and minimizing morbidity in pediatric tongue tumors. This review summarizes current knowledge aimed at enhancing differential diagnosis and management of these diverse entities.

Serum microRNAs as biomarkers associated with the course of diabetic macular oedema

Aims/Purpose: Intravitreal drugs are powerful therapies for diabetic macular edema (DME), but the clinical course and the response to treatments still remains unpredictable. MicroRNAs (miRNAs) represent a class of small molecules associated with the regulation of several metabolic pathways, including diabetes. However, their use in the clinical setting of DME is limited by lack of data regarding the correlation with clinical and imaging features. In the present study we evaluated some miRNAs profiles obtained by serum samples of newly diagnosed DME patients.Methods: Prospective study, including baseline visit (V0), a 4‐months visit (V1) and a 12‐months visit (V2). At V0 patient underwent serum sampling, imaging was repeated at every study visit. Enriched serum microRNAs were extracted and real‐time PCR was performed. OCT features were used to assess the severity and progression of the disease.Results: We collected data of a cohort of type 1 and type 2 diabetic patients. All the patients presented DME and were naïve to intravitreal treatments. The follow‐up was of 12 months. Diabetes‐related miRNAs were found up‐ or downregulated and associated with the course of DME (resolved/recurrent/persistent). Correlations were found with imaging biomarkers.Conclusions: On the basis of these findings, serum miRNAs assessment is a feasible and clinically relevant assessment to be performed in the diagnostic work‐up of diabetic patients affected by DME. MiRNAs might pave the basis for customized treatment strategies to optimize the management of DME.

Basic Management Strategies by Life Cycle for Treatment of the Persons With Autism Spectrum Disorder.

Interventions for targeted symptoms are important when setting treatment strategies for individuals with autism spectrum disorder (ASD) and developmental disabilities. Especially, the goal should be to achieve individual "niche construction" by allowing them to select and adjust an environment where they can demonstrate their special characteristics and strengths. In addition, these choices should vary depending on the stage of development of each person with ASD and developmental disabilities. It is necessary to establish a detailed and systematic plan for diagnosis and treatment necessary for infants and toddlers, school placement in school age, and employment or self-reliance in adult transition period to establish customized treatment strategies that fit the individual level of people with ASD and developmental disabilities.

A comparative study of clustering methods on gene expression data for lung cancer prognosis

Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two lung adenocarcinoma datasets. For each method, we clustered patients into two clusters and assessed the difference in patient survival time between clusters. Unsupervised methods were found to have large logrank p-values and no significant results in most cases. Semi-supervised and supervised methods had improved performance over unsupervised methods and very significant p-values. These results indicate that unsupervised methods are not capable of identifying clusters with significant differences in survival prognosis in most cases, while supervised and semi-supervised methods can better cluster patients into clinically useful subtypes.

Direct antimicrobial effects of chemokines on Cryptococcus spp, with special emphasis on a 'CXC' chemokine.

Cryptococcus species are ingenious human pathogens that are widespread globally. They continue to cause over 200,000 deaths per year. Presently due to the rise in resistance and therapy failure, it is necessary to shift the focus to an alternate therapeutic strategy against this pathogen. One promising approach is to emphasize the host defense system in order to develop more precise and customized treatment strategies. In this regard, research has revealed that interferon-γ-inducible CXCL10 chemokine, amongst other chemokines spanning both CXC and CC categories, has a direct killing effect in vitro against Cryptococcus neoformans and Cryptococcus gattii, with a significantly greater microbicidal effect against the former. Moreover, when CXCL10 is used in combination with CCL5, there is a significant reduction in the survival of C. gattii at normal-serum level concentration, indicating a previously unreported synergistic effect of these two chemokines. Confocal and STED microscopic studies have demonstrated that CXCL10 has both cell wall/membrane and intracellular targets against this fungus. These findings present new possibilities for developing chemokine-derived small molecule antifungals and may represent a step forward in creating precision medicine tailored to each patient.

Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes.

Ketosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develop and validate a novel nomogram to predict KPD. In this retrospective study, clinical data of a total of 398 newly diagnosed type 2 diabetes in our hospital who met our research standards with an average age of 48.75 ± 13.86 years years old from January 2019 to December 2022 were collected. According to the occurrence of ketosis, there were divided into T2DM groups(228 cases)with an average age of 52.19 ± 12.97 years, of whom 69.74% were male and KPD groups (170cases)with an average age of 44.13 ± 13.72 years, of whom males account for 80.59%. Univariate and multivariate logistic regression analysis was performed to identify the independent influencing factors of KPD and then a novel prediction nomogram model was established based on these independent predictors visually by using R4.3. Verification and evaluation of predictive model performance comprised receiver-operating characteristic (ROC) curve, corrected calibration curve, and clinical decision curve (DCA). 4 primary independent predict factors of KPD were identified by univariate and multivariate logistic regression analysis and entered into the nomogram including age, family history, HbA1c and FFA. The model incorporating these 4 predict factors displayed good discrimination to predict KPD with the area under the ROC curve (AUC) of 0.945. The corrected calibration curve of the nomogram showed good fitting ability with an average absolute error =0.006 < 0.05, indicating a good accuracy. The decision analysis curve (DCA) demonstrated that when the risk threshold was between 5% and 99%, the nomogram model was more practical and accurate. In our novel prediction nomogram model, we found that age, family history, HbA1c and FFA were the independent predict factors of KPD. The proposed nomogram built by these 4 predictors was well developed and exhibited powerful predictive performance for KPD with high discrimination, good accuracy, and potential clinical applicability, which may be a useful tool for early screening and identification of high-risk population of KPD and therefore help clinicians in making customized treatment strategy.

Clinical Features and Prognoses of Patients With Breast Cancer Who Underwent Surgery

Breast cancer (BC) remains a pervasive malignant neoplasm worldwide, with increasing incidence. However, there are a scarcity of studies examining the clinical characteristics and prognosis of Chinese patients with BC who have undergone surgery. To evaluate overall survival (OS) and disease-free survival (DFS) in patients with surgically treated BC in China, focusing on histopathology and surgical approach. This cohort study included a retrospective review of the medical records of patients with unilateral BC who underwent surgery between January 2009 and September 2017, with a median follow-up time of 7.69 years. Clinical features were extracted from these records, and survival analysis was performed. Data analysis was conducted in March 2023. Patients' OS and DFS. The study included 14 782 patients (14 724 [99.6%] female patients; mean [SD] age, 51.6 [10.9] years). Invasive ductal carcinoma (IDC) was the most prevalent type, observed in 12 671 patients (85.6%). Stages 0, I, II, III, and IV accounted for 6.4% (919 patients), 32.0% (4579 patients), 40.5% (5791 patients), 20.2% (2896 patients), and 0.9% (126 patients) of cases, respectively. Hormone receptor (HR) positivity was observed in 10 241 patients (75.1%), and 3665 (29.1%) tested positive for ERBB2 (formerly HER2/neu). The HR-negative-ERBB2-negative, HR-negative-ERBB2-positive, HR-positive-ERBB2-negative, and HR-positive-ERBB2-positive subtypes constituted 13.3% (1666 patients), 12.7% (1595 patients), 57.8% (7251 patients), and 16.2% (2034 patients) of cases, respectively. Breast-conserving surgery (BCS) was performed in 2884 patients (19.5%). The 5-year and 10-year OS rates were 92.9% (13 689 of 14 732) and 87.4% (3287 of 3760), while the 5-year and 10-year DFS rates were 89.0% (12 916 of 14 512) and 82.9% (3078 of 3713), respectively. Multivariate analysis found that for patients with IDC, age, BCS, invasive tumor size, tumor grade, lymphovascular invasion (LVI), the number of lymph node metastases (LNMs), distant metastasis, Ki67, and HR status were associated with OS, whereas invasive tumor size, tumor grade, LVI, the number of LNMs, HR status, and ERBB2 status were associated with DFS. After propensity score matching, BCS was equivalent to mastectomy with respect to survival in patients with IDC. This cohort study of patients with BC who underwent surgery in China provides valuable insights into the histopathological characteristics and survival outcomes of this population. The diverse histopathological features emphasize the necessity for customized treatment strategies. The relatively low BCS rate in the study population suggests the need for heightened awareness and adoption of this approach, considering its potential advantages for survival.

Transforming Healthcare: Harnessing the Power of AI in the Modern Era

Patient care and medical research are changing as a result of the application of artificial intelligence (AI) in healthcare. To fully realize the potential of AI technology, collaborative relationships between AI developers and healthcare providers are essential. This study examines the advantages and prospects of collaborating with healthcare professionals to improve healthcare outcomes. Personalized medicine, clinical decision support systems, healthcare process optimization, patient engagement, and ethical considerations are just a few of the areas where AI and healthcare practitioners are collaborating. Significant progress can be made by fusing the knowledge of healthcare professionals with AI's powers in data analysis, pattern recognition, and predictive modeling. Advancements in diagnosis and therapy are a major area of collaboration. Healthcare practitioners can gain from enhanced diagnostic precision, early illness identification, and exact treatment planning by integrating AI algorithms with patient data. Enhanced patient outcomes and improved healthcare delivery are the outcomes. The development of personalized medicine techniques is also made possible by collaboration. Healthcare professionals can customize treatment strategies based on unique genetic markers, biomarkers, and clinical factors by utilizing AI algorithms to examine patient data. This collective effort results in improved treatments and treatment outcomes. Clinical decision support system development is facilitated by collaborations between AI and healthcare professionals. By analyzing patient data, medical literature, and clinical recommendations using AI technology, these systems offer real-time guidance to medical personnel. Clinical decision support systems increase the effectiveness of diagnosis, the choice of treatment, and patient safety by strengthening decision-making abilities. In healthcare settings, collaboration also emphasizes process improvement, increasing effectiveness, and resource management. Artificial intelligence (AI) algorithms can examine operational data and patient flow patterns to spot inefficiencies, resulting in the simplification of administrative work, enhanced patient scheduling, and better resource management. Costs are reduced, operational effectiveness is raised, and patient experiences are improved as a result. Patient participation and experience are another facet of partnership. Artificial intelligence-enabled virtual assistants and catboats offer individualized support, respond to patient questions, and deliver health information. These resources improve patient satisfaction, ease of access to healthcare, and patient empowerment in health management. AI and healthcare practitioners working together must take ethical issues and legal compliance very seriously. It is crucial to protect patient privacy, guarantee data security, and abide by ethical standards and regulatory frameworks. Collaborations can improve healthcare results and preserve patient trust by taking these factors into account. AI and healthcare providers working together could change how patients are treated, promote medical research, and enhance patient outcomes. Partnerships that make use of AI technologies and integrate them with healthcare knowledge promote innovation, improve patient engagement, optimize diagnostic and therapeutic procedures, and ensure ethical and legal compliance. AI and healthcare professionals work together continuously to enhance patient outcomes and the standard of care, shaping the future of healthcare delivery.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs

ObjectivesTo develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. MethodsReference segmentations were obtained on CTs (2006–2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. ResultsTotal 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1–12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). ConclusionA computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. Clinical relevanceAI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.