Optimal Treatment Plan Research Articles

Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.

Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Keslingsetup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.

Evaluation of effects of different sizes and shapes of attachments during rotation, tipping, and torquing in clear aligner therapy - A finite element study.

To evaluate the effect of different shapes and sizes of attachments used for rotation, tipping, and torquing tooth movement in clear aligner therapy. Using computer-aided design/computer-aided manufacturing technology, 15 replicas of the upper right human canines were produced. Finite element analysis (FEA) was utilized to mimic tooth movement by applying a constant force of 2.942 N to these models, which contained various forms and sizes of composite attachments. Analysis was done on the stress on different dental structures and the displacement of the attachments. The outcomes demonstrated that tooth movement during clear aligner treatment was considerably impacted by attachment size and shape. Rectangular beveled attachments with dimensions of 3.5 mm × 1.2 mm × 3.5 mm showed the greatest rotating mobility. Triangular beveled attachments with dimensions of 5 mm by 0.8 mm by 2 mm produced the most displacement during tipping motion. For torquing motions, rectangular power ridge attachments with dimensions of 0.5 mm × 0.5 mm × 5 mm worked well. The size and geometry of composite attachments were shown to be extremely important in regulating tooth movement during clear aligner treatment, according to this FEA research. Greater displacement is produced when smaller attachments with less surface area dissipate more force. When using clear aligner therapy, orthodontists may optimize treatment plans, shorten treatment times, and provide more predictable results by having a better understanding of the biomechanics of various attachment designs.

P014 APPLICATION OF AI IN HYPERTENSION MANAGEMENT: A REVIEW

Background and Objective: The integration of artificial intelligence (AI) in healthcare has revolutionized the management of various diseases, including hypertension. This review explores the potential of AI applications in improving hypertension diagnosis, treatment, and patient outcomes. Methods: A comprehensive literature review was conducted using databases such as PubMed, Scopus, and IEEE Xplore. Studies published between 2010 and 2023 were included, focusing on AI models used for hypertension management, such as machine learning algorithms and predictive analytics. Results: AI has demonstrated significant accuracy in predicting hypertension risk factors, optimizing treatment plans, and monitoring patient adherence. Machine learning models, such as neural networks and decision trees, have shown up to 95% accuracy in early hypertension detection. Furthermore, AI-driven personalized treatment plans have resulted in improved patient outcomes and reduced healthcare costs. Conclusions: AI holds tremendous potential in transforming hypertension management by enhancing diagnostic precision and personalizing treatment strategies. Future research should focus on integrating AI with electronic health records and developing real-time monitoring systems to further improve patient care.

Atopic Dermatitis (Eczema) Guidelines 2023: Highlights

Atopic Dermatitis (AD) is one of the most common chronic inflammatory skin disorders with a complex pathogenesis. AD is characterized by eczematous skin lesions, pruritus, and recurrent skin infections; with negative impact on patients (and caregivers) quality of life.The American Academy of Allergy, Asthma and Immunology (AAAAI)/American College of Allergy, Asthma and Immunology (ACAAI) Joint Task Force (JTF) Atopic Dermatitis Guideline Panel recently released updated AD guidelines. This guideline focuses on addressing clinical questions using trustworthy guideline development standards, including mitigating the potential influence of financial and non-financial conflicts of interest, and GRADE methodology. A multidisciplinary panel used systematic reviews and meta-analyses to inform specific recommendations addressing optimal use of (1) topical treatments, (2) dilute bleach bath, (3) dietary avoidance/elimination, (4) allergen immunotherapy and (5) systemic treatments. The comprehensive recommendations, emphasizing the third principle of evidence-based medicine - that evidence alone is never enough; that patient values and preferences must be carefully considered when determining optimal treatments for patients and populations - provide a framework to support clinicians in selecting an optimal treatment plan for each patient.This review provides an overview of the guideline and discusses how those recommendations relate to current practice.

272. HOSPITAL VARIATION IN PALLIATIVE TREATMENT FOR ESOPHAGEAL AND GASTROESOPHAGEAL JUNCTION CANCER: A NATIONWIDE POPULATION-BASED STUDY IN THE NETHERLANDS

Abstract Background Palliative care for patients with esophageal (EC) includes a large variety of treatments and data on variation of palliative care across centers are unknown. This study aims to evaluate treatment variation at the hospital level for EC and GC patients in the Netherlands. Methods A nationwide retrospective cohort study was conducted using population-based data from the Netherlands Cancer Registry. Patients diagnosed with inoperable advanced (cT4B) or synchronous metastatic (cM1) EC between 2015-2022 were included. Probability of receiving potentially life-prolonging treatment (palliative resection, chemoradiotherapy or systemic treatment) according to hospital of diagnosis was analyzed using multilevel logistic regression analysis. Hospitals were categorized in tertiles according to this probability (high, medium, low) and overall survival (OS) was assessed using cox regression analysis. Results 8,580 EC patients were included. Potentially life-prolonging treatment ranged from 23.5%-74.0% across hospitals of diagnosis (p&amp;lt;0.0001). After adjustment for patient and tumor characteristics, the probability of receiving these treatments was associated with hospital of diagnosis (p&amp;lt;0.0001). 13 of 79 hospitals demonstrated a significantly lower probability of giving potentially life-prolonging treatment. Patients diagnosed in hospitals with low probability of potentially life-prolonging treatment with had significantly worse OS than medium- (HR 0.85 (0.80-0.90) p&amp;lt;0.0001) or high-probability hospitals (HR 0.77 (0.73-0.82) p&amp;lt;0.0001). Conclusion This study shows substantial variation in palliative treatment for EC and GC care across Dutch hospitals, emphasizing the challenge of defining optimal treatment plans. Hospital of diagnosis was significantly associated with the probability of receiving potentially life-prolonging treatment and also with overall survival. These results suggest that appropriate selection of patients for treatment with potentially life-prolonging treatment leads to improved OS.

Prognostic value of baseline clinicopathological characteristics in first-line chemotherapy ± immunotherapy for extensive-stage small cell lung cancer: a retrospective cohort study.

The integration of chemotherapy and immunotherapy as a first-line treatment for extensive-stage small cell lung cancer (ES-SCLC) has been adopted in clinical practice, yet the response to immune checkpoint inhibitors (ICIs) is variable, benefiting only a fraction of patients. The current absence of reliable biomarkers for predicting treatment response and prognosis represents a significant gap in knowledge, hindering the optimization of patient stratification and treatment planning. This retrospective cohort study aims to assess the potential predictive and prognostic significance of clinicopathological baseline features in ES-SCLC patients. Our study retrospectively analyzed the data of consecutive patients with ES-SCLC treated with first-line etoposide plus platinum chemotherapy ± immunotherapy at The Affiliated Lihuili Hospital of Ningbo University from April 2017 to April 2023. Data on clinical information, serum laboratory indicators, pathological immunohistochemical markers, and progression-free survival (PFS) times were collected. Univariate and multivariate Cox regression analyses were employed to determine whether these indicators could serve as independent prognostic factors for PFS. Further, potential predictive markers for treatment efficacy were identified using a Cox regression model that incorporated an interaction term between treatment modality and the indicator. A total of 121 patients with ES-SCLC were enrolled in the study, of whom 62 received chemotherapy alone, and 59 received chemotherapy in combination with immunotherapy. Compared to chemotherapy alone, the addition of immunotherapy to first-line chemotherapy significantly extended the PFS time [P<0.001; hazard ratio (HR) =0.42; 95% confidence interval (CI): 0.28, 0.64] of the ES-SCLC patients. The multivariate analysis revealed that an immunochemotherapy regimen (P<0.001, HR =0.40; 95% CI: 0.24, 0.68), a low-density lipoprotein (LDL) level of >1.8 mmol/L (P=0.02; HR =0.41; 95% CI: 0.20, 0.85) were independent prognostic factors of favorable PFS in the first-line treatment of all ES-SCLC, while a lactate dehydrogenase (LDH) level of >273 U/L (P=0.04; HR =1.78; 95% CI: 1.03, 3.07), a neuron-specific enolase (NSE) concentration of >102.6 ng/mL (P=0.009; HR =6.49; 95% CI: 1.60, 26.32), an apolipoprotein A1 (ApoA1) concentration of >0.9 g/L (P<0.001; HR =4.15; 95% CI: 1.98, 8.71), and an apolipoprotein B (ApoB) concentration of >0.8 g/L (P=0.002; HR =2.24; 95% CI: 1.34, 3.75) were independent prognostic factors of poorer PFS. Further, the interaction effect analysis demonstrated that an LDL level of >1.8 mmol/L and the absence of bone metastasis were potential predictors of an improved response to ICI therapy compared to chemotherapy alone. This study showed the survival benefit of receiving a chemoimmunotherapy regimen as the first-line treatment in a real-world scenario. It also suggests the prognostic significance of pre-treatment LDL, LDH, NSE, ApoA1, and ApoB with optimal cut-off values in the first-line treatment of all ES-SCLC, and the potential utility of baseline LDL level or the presence of bone metastasis in guiding first-line treatment strategies.

Innovative Research on the Integration of Traditional Chinese Medicine and Artificial Intelligence for the Treatment of Herpes Zoster

Herpes zoster is a skin disease caused by the varicella-zoster virus (VZV), primarily manifesting as painful skin rashes in localized areas. Traditional Chinese medicine has a long history of treating herpes zoster, and the integration of modern artificial intelligence (AI) technologies holds promise for further enhancing treatment efficacy. This study aims to construct an AI-based traditional Chinese medicine treatment plan by analyzing large-scale clinical data and traditional medical literature to establish individualized diagnostic models. The experimental results demonstrate that this method effectively shortens the disease course and reduces the incidence of complications. Additionally, the study reveals the mechanisms of action of various components of traditional Chinese medicine in treatment, providing a basis for further optimization of treatment plans. This innovative integration approach not only improves treatment precision but also offers a new paradigm for combined traditional and Western medicine treatment. This paper not only showcases the potential of AI in the field of traditional Chinese medicine but also provides new insights into the treatment of herpes zoster.

Navigating the Integration of Machine Learning in Healthcare: Challenges, Strategies, and Ethical Considerations

The amalgamation of artificial intelligence (AI) and machine learning (ML) in healthcare offers a revolutionary prospect to improve patient outcomes, optimize workflow, and curtail expenses. Robust computational resources, competent interdisciplinary teams, and careful management of high-quality, diverse datasets are necessary for the successful deployment of ML models. To preserve public confidence and guarantee adherence to legal requirements, ethical issues, specifically those pertaining to patient consent, data privacy, and confidentiality are crucial. Regulatory bodies, legislators, and healthcare providers must all be involved in stakeholder engagement to establish a supportive environment for ML integration. The research highlights the significance of transparency and interpretability, promoting explainable AI models that augment clinician trust and enable wider adoption in the medical community. This thorough analysis finds important gaps in the current state of ML applications in healthcare and highlights new trends. It discusses ethical ramifications, realistic implementation guidelines, and the need for cooperative stakeholder engagement. The goal of the paper is to close the “translation gap” between model development and clinical application by examining real-world applications and offering a framework for the methodical integration of ML in clinical settings. The study promotes models that are not only technically sound but also user-friendly and in line with the practical needs of healthcare professionals by emphasizing the human-centric design of ML systems. By facilitating accurate and timely diagnoses, optimizing treatment plans, and eventually raising the standard of healthcare services as a whole, the strategic application of ML in the medical field holds the potential to completely transform patient care. This study opens the door to a future in which AI-driven healthcare solutions are smoothly integrated into routine clinical practice by providing useful insights and guidelines to enhance trustworthy and moral integration.

Surgical Outcomes for Patients With Trisomy 21 and Hirschsprung's Disease: An NSQIP-Pediatric Study

IntroductionPrevious studies have demonstrated worse outcomes for Hirschsprung's disease (HD) procedures in Trisomy 21 (T21) patients. Using a large national database, we sought to investigate surgical outcomes in HD patients with T21 compared to non-T21 patients. MethodsWe utilized the deidentified National Surgical Quality Improvement Program Pediatric database from 2012 to 2021. Using International Classification of Diseases, Ninth Revision codes, children <18 y old with HD were included and stratified by T21 diagnosis. Demographics, Current Procedural Terminology codes, case characteristics, length of hospital stay, and postoperative complications were analyzed. ResultsOf 3456 HD patients, 12.0% (n = 413) patients had a concurrent diagnosis of T21. Pull-through (PT) procedures accounted for 54.9% of surgeries (n = 1896), of which 10.0% (n = 189) had T21. T21 patients who underwent PT had a younger gestational age (P < 0.0001), cardiac risk factors (P < 0.0001), hematologic disorders (P < 0.0001), higher American Society of Anesthesiologists class (P < 0.0001), and were older at their index operation (P = 0.03). Though operative times were similar, T21 patients had a longer total length of stay (P = 0.0263), postoperative length of stay (P = 0.0033), and more unplanned reoperations (P = 0.0094). Though only significant in unadjusted analyses, T21 patients had more postoperative complications after PT (P = 0.0034), specifically deep surgical site infections (P = 0.009), organ/space surgical site infections (P = 0.004), wound disruption (P < 0.001), and sepsis (P = 0.025). ConclusionsWe confirm significant differences exist between T21 and non-T21 patients undergoing HD procedures, particularly increased total length of stay, postoperative length of stay, and unplanned reoperations. Understanding these differences will lead to more optimal treatment plans for this unique patient population.

Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism

ObjectiveThis study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model. MethodsFrom The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model. ResultsMultivariable cox analysis (HR = 1.488, 95 % CI 1.012–2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008–2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001). ConclusionsThe predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).

Cellular therapy site-preparedness: Inpatient pharmacy implementation at a large academic medical center.

With the recent Food & Drug Administration (FDA) approval of cellular therapy that requires product manipulation prior to administration in combination with a short stability window, the need was identified for local dose preparation within the pharmacy rather than the off-site stem cell processing laboratory. This approval gave rise to assessment of regulatory standards surrounding cellular therapy, evaluation and revision of current standard operating procedures and policies with formal process validation, assessment of occupational exposure mitigation and safety considerations, and development of staff training and education. To describe and provide insight into the stepwise process of FACT validation and onboarding of commercially available cellular therapy products that require sterile compounding manipulation within a pharmacy prior to administration. A multidisciplinary effort is required to attain FACT certification and implement pharmacist compounding of cellular therapy products.1 Local preparation within a pharmacy facilitates a sound operational workflow and provides a pathway to perform aseptic manipulations of cellular therapy products safely and efficiently. Safe and successful administration of cellular therapies handled and compounded by pharmacy department staff along with program validation requires a preemptive review utilizing a multidisciplinary approach for process development. This manuscript will provide a foundation based on consistency and transparency in effective cellular therapy sterile compounding and aseptic manipulation, proper handling and disposal procedures, increased communication through creation and optimization of treatment plans and order-sets, standardized medical center staff education, and development of policies and standard operating procedures for the entire health care team.

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients-surgeons, medical oncologists, and radiation oncologists-on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

Review and adaptation of European Federation of Internal Medicine clinical guidelines on acute heart failure

The article discusses the adaptation of European Federation of Internal Medicine clinical guidelines for the management of patients with acute heart failure (AHF). An algorithm has been proposed for the differential diagnostic examination of patients with AHF and acute shortness of breath, signs of congestion, hypoxemia, including the determination of natriuretic peptides (NUPs) and/or cardiac troponin, assessment of the congestion severity using echocardiography, as well as the potential of chest radiography and lung ultrasound in certain clinical situations. Special attention is paid to methods for assessing and treating signs of fluid congestion, which have the most accurate prognostic value in patients hospitalized due to AHF. Assessment of the prognosis and the need for hospitalization of AHF patients in the intensive care unit are highlighted. Treatment strategies for patients with hypotension and low cardiac output are discussed. The latest guidelines for the treatment of patients with AHF, taking into account concomitant diseases, are presented. Indications for hospital discharge of patients with AHF, optimization of the further treatment plan, effective strategies for reducing the risk of rehospitalization and mortality, both at the hospital discharge and outpatient stage, are discussed.

The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis

Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realised.

Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment.

The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes.For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.

DRN-CDR: A cancer drug response prediction model using multi-omics and drug features

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug – Cell line pairs. The proposed method yielded higher Pearson’s correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

Incorporating intensity modulated total body irradiation into a Children's Oncology Group trial: Rationale, techniques, and safeguards.

Historically, total body irradiation (TBI) has been delivered using static, parallel opposed photon beams (2D-TBI). Recently, centers have increasingly used intensity-modulated radiation therapy (IMRT) techniques for TBI. Relative to 2D-TBI, IMRT can reduce doses to critical organs (i.e., lungs and kidneys) while delivering myeloablative doses to the rest of the body, so it may decrease the risk of toxicity while maintaining oncologic outcomes. Despite these potential benefits, delivering TBI using IMRT introduces new challenges in treatment planning and delivery. We describe the extensive experience with IMRT-based TBI at Stanford University and City of Hope Cancer Center. These groups, and others, have reported favorable clinical outcomes and have developed methods to optimize treatment planning and delivery. A critical next step is to evaluate the broader adoption of this approach. Therefore, IMRT-based TBI will be incorporated into a prospective, multi-institutional Children's Oncology Group study with careful procedures and safeguards in place.

Predicting Metformin Efficacy in Improving Insulin Sensitivity Among Women With Polycystic Ovary Syndrome and Insulin Resistance: A Machine Learning Study

ObjectiveMetformin is clinically effective in treating polycystic ovary syndrome (PCOS) with insulin resistance (IR), while its efficacy varies among individuals. This study aims to develop a machine learning model to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. MethodsThis is a retrospective analysis of a multicenter, randomized controlled trial involving 114 women diagnosed with PCOS and IR. All women received metformin treatment for 4 months. We incorporated 27 baseline clinical variables of the women into the construction of our machine learning model. We firstly compared 4 commonly used feature selection methods to screen valuable clinical variables. Then we used the valuable variables as inputs to evaluate the performance of 5 machine learning models, including k-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, and Extreme Gradient Boosting, in predicting the efficacy of metformin. ResultsAmong the 5 machine learning models, Support Vector Machine performed the best with an area under the receiver operating characteristic curve of 0.781 (95% confidence interval [CI]: 0.772-0.791). The key predictive variables identified were homeostasis model assessment of insulin resistance, body mass index, and low-density lipoprotein cholesterol. ConclusionThe developed machine learning model could be applied to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. The result could help doctors evaluate the efficacy of metformin in advance, optimize treatment plans, and thereby enhance overall clinical outcomes.

Analyzing RPM and RGSC Infrared Camera Sensitivity: A Comparative Study of Breathing Cycle Replication Verifications and Limitations.

This study addresses challenges in delivering high radiation doses and managing organ motion in Stereotactic Body Radiation Therapy (SBRT) for thoracic and abdominal cancer. It evaluates Varian's Real Time Position Management (RPM) system's infrared camera sensitivity during crucial Four-Dimensional computed tomography (4D-CT) scans for planning and treatment. The analysis includes CT simulator, LINAC (Novalis Tx and TrueBeam STx). This research enhances SBRT precision by offering insights into RPM and RGSC system performance across machines, impacting treatment planning and delivery optimization. The QUASAR™ Respiratory Motion Assembly phantom is aligned with precision using lasers. It is configured with either six-dot reflective or four-dot lens marker blocks featuring a retroreflective marker placed on the phantom's surface. Motion is induced by adjusting the amplitude, and the camera position is finely tuned to monitor the marker's movements. This investigation entails variations in seconds per breath (SPB) within the Quasar breath platform, specifically at intervals of 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 seconds while maintaining a 1cm amplitude camera setting. For TrueBeam-STx: Ensure SPB values are kept above 1.8 seconds for accurate replication. For Novalis-Tx: Stay within an SPB range of up to 2.0 seconds for reliable reproducibility. For CT Simulator: Optimal replication up to an SPB of 2.2 seconds; avoid SPB values below 1.8 seconds for reliable detection. Data for TrueBeam-STx, Novalis-Tx, and the CT simulator shows discrepancies in replicating the breathing cycle as Seconds Per Breath (SPB) decreases. Effective Infrared (IR) sensitivity is observed until SPB thresholds: 1.8s (TrueBeam-STx), 2.2s (Novalis-Tx), and 2.2s (CT simulator). We should consider values equal to or greater than the mentioned breathing periods. Variations in replicating breathing cycles signal challenges in planning and delivering treatments, especially with lower SPB values. These insights guide clinicians to adapt treatments based on machine-specific capabilities for accurate and reproducible outcomes.

Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research

BackgroundPatients with fractures of the proximal humerus often local complications and failures attributed to osteoporosis. Currently, there is a lack of straight forward screening methods for assessing the extent of local osteoporosis in the proximal humerus. This study utilizes machine learning techniques to establish a diagnostic approach for evaluating local osteoporosis by analyzing the patient's demographic data, bone density, and X-ray ratio of the proximal humerus. MethodsA cohort comprising a total of 102 hospitalized patients admitted during the period spanning from 2021 to 2023 underwent random selection procedures. Resulting in exclusion of 5 patients while enrolling 97 patients for analysis encompassing patient demographics, shoulder joint anteroposterior radiographs, and bone density information. Using the modified Tingart index methodology involving multiple measurements denoted as M1 through M4 obtained from humeral shafts. Within this cohort comprised 76 females (78.4 %) and 21 males (21.6 %), with an average age of 73.0 years (range: 43–98 years). There were 25 cases with normal bone density, 35 with osteopenia, and 37 with osteoporosis. Machine learning techniques were used to randomly divide the 97 cases into training (n = 59) and validation (n = 38) sets with a ratio of 6:4 using stratified random sampling. A decision tree model was built in the training set, and significant diagnostic indicators were selected, with the performance of the decision tree evaluated using the validation set. Multinomial logistic regression methods were used to verify the strength of the relationship between the selected indicators and osteoporosis. ResultsThe decision tree identified significant diagnostic indicators as the humeral shaft medullary cavity ratio M2/M4, age, and gender. M2/M4 ≥ 1.13 can be used as an important screening criterion; M2/M4 < 1.13 was predicted as local osteoporosis; M2/M4 ≥ 1.13 and age ≥83 years were also predicted as osteoporosis. M2/M4 ≥ 1.13 and age <64 years or males aged between 64 and 83 years were predicted as the normal population; M2/M4 ≥ 1.13 and females aged between 64 and 83 years were predicted as having osteopenia. The decision tree's accuracy in the training set was 0.7627 (95 % CI (0.6341, 0.8638)), and its accuracy in the test set was 0.7895 (95 % CI (0.6268, 0.9045)). Multinomial logistic regression results showed that humeral shaft medullary cavity ratios M2/M4, age, and gender in X-ray images were significantly associated with the occurrence of osteoporosis. ConclusionUtilizing X-ray data of the proximal humerus in conjunction with demographic information such as gender and age enable the prediction of localized osteoporosis, facilitating physicians' rapid comprehension of osteoporosis in patients and optimization of clinical treatment plans. Level of evidenceLevel IV retrospective case study.