Individualized Treatment Regimens Research Articles

CTNI-49. PRECISION MEDICINE CLINICAL TRIAL FOR ADULT PATIENTS WITH RECURRENT GLIOBLASTOMA

Abstract Salvage therapies for recurrent glioblastoma (rGBM) have limited efficacy, with median overall survival of 9 months (OS-9) and 6-month progression-free survival (PFS-6) of 10-25%. Given GBM’s molecular heterogeneity, targeting a single molecular abnormality in isolation has consistently failed as a strategy, and precision combination approaches are needed. An initial 15-patient cohort evaluated the feasibility and safety of treating patients with rGBM with an individualized treatment regimen; we added a second 15-patient expansion cohort to improve the power of preliminary efficacy assessment. Patients underwent clinically indicated surgery with genomic profiling using the UCSF500 targeted DNA sequencing panel. Each personalized treatment regimen combined up to 4 FDA-approved drugs, including one cytotoxic agent, as determined by consensus discussion at an individualized molecular tumor board for each patient. Bevacizumab was not utilized in these combinations. The primary objective of the combined 30-patient cohort was efficacy of individualized treatment regimens by OS-9; secondary objectives included safety and additional efficacy measures including PFS-6. Median age was 53 years (range 32-71). Of 30 patients, 18 were male, 23 were enrolled at first recurrence, 29 had IDH-wildtype glioblastoma, 1 had IDH-mutant astrocytoma, and 10 had MGMT promoter methylation. A total of 12 FDA-approved drugs were used in 18 combinations; the most common regimen prescribed was lomustine, afatinib, and abemaciclib (7; 23%,); second-most common was temozolomide, olaparib, and afatinib (4; 13%). OS-9 of the combined cohort was 70% (95% CI 0.55 – 0.89) and PFS-6 was 27% (95% CI 0.15 – 0.48), with median OS of 11.6 months (95% CI 9.7 – 16.7) and median PFS of 3.7 months (95% CI 2.0 – 5.5).While overall efficacy was modest, implementation of individualized treatment regimens in a timely fashion was safe and feasible for patients with surgically resectable rGBM. Analysis is ongoing to assess whether specific genomic profiles or treatment regimens were particularly efficacious.

Utility of patient-derived xenografts to evaluate drug sensitivity and select optimal treatments for individual non-small-cell lung cancer patients

Abstract Background Patient-derived xenograft (PDX) is currently considered a preferred preclinical model to evaluate drug sensitivity, explore drug resistance mechanisms, and select individualized treatment regimens. Methods Histopathological examination, immunohistochemistry and whole-exome sequencing confirmed similarity between our PDX tumors and primary tumors in terms of morphology and genetic characteristics. The drug reactivity of the PDX tumor was validated in vivo. The mechanisms of acquired resistance to Osimertinib PDX tumors were investigated by WES and WB. Results We successfully established 13 NSCLC-PDXs derived from 62 patients, including eight adenocarcinomas, four squamous-cell carcinoma, and one large-cell neuroendocrine carcinoma. Histological subtype and clinical stage were significant factors affecting the successful PDXs establishment. The treatment responses to conventional chemotherapy in PDXs were entirely consistent with that of their corresponding patients. According to the genetic status of tumors, more appropriate targeted agents were selected in PDXs for their corresponding patients as alternative treatment options. In addition, a PDX model with acquired resistance to osimertinib was induced, and the overactivation of RAS mitogen-activated protein kinase (MAPK)-extracellular signal-regulated kinase (ERK) signaling pathway caused by the dual-specificity phosphatase 6 (DUSP6) M62I mutation was found to play a key role in the development of osimertinib resistance. Trametinib, a specific inhibitor of the MAPK-ERK pathway significantly slowed down the tumor growth in osimertinib-resistant PDX models, providing an alternative treatment in patients after osimertinib failure.

Risk factors of cervical central lymph node metastasis in stage T1a unifocal papillary thyroid carcinoma

To investigate the correlation of cervical central lymph node metastasis (CLNM) in stage T1a unifocal papillary thyroid carcinoma (PTC) with the clinicopathological characteristics, ultrasonography features and the number of lymph node dissection, and to analyze the risk factors of CLNM. Data from 493 unifocal PTC patients (T1a) who underwent partial or total thyroidectomy and pCLND at the Guizhou Provincial People’s Hospital were collected and retrospectively analyzed. They were divided into two groups in accordance with cervical CLNM or not. Their information, including clinical characteristics, ultrasound (US) features, pathological results, and other characteristics of the groups, was analyzed and compared using univariate and multivariate logistic regression analyses. A total of 493 patients were eligible in this study. Among them, 33.7% (166/493) of PTC patients had cervical CLNM, and 66.3% (327/493) did not. The two groups were compared using a univariate analyses, and there were no significant differences between the two groups in age, maximum tumor size, tumor location, aspect ratio, boundary, morphology, echogenicity, BRAFV600E and HT (P > 0.05), and there were significant differences between gender, capsule contact, microcalcifications, rich vascularity, and number of lymph node dissection (P < 0.05). A multivariate logistic regression analyses was performed to further clarify the correlation of these indices. However, only male (OR = 1.770, P = 0.009), microcalcifications (OR = 1.791, P = 0.004), capsule contact (OR = 1.857, P = 0.01), and number of lymph node dissection (OR = 2.274, P < 0.001) were independent predictors of cervical CLNM. In conclusion, four independent predictors of cervical CLNM, including male, microcalcifications, capsule contact, and number of lymph node dissection, were screened out. Therefore, a comprehensive assessment of these risk factors should be conducted when designing individualized treatment regimens for PTC patients.

Role of polypill in cardiovascular prevention and treatment.

Cardiovascular diseases (CVD) remain the leading cause of mortality globally and require innovative strategies for effective prevention and treatment. The polypill concept, which integrates multiple cardioprotective agents into a single dosage form, has emerged as a promising approach to improve adherence and simplify the management of cardiovascular risk factors. We review clinical trials and observational studies evaluating the impact of the polypill on reducing the incidence of major cardiovascular events (MACEs), its influence on medication adherence, and its potential to fill treatment gaps in diverse populations. Also of note are the pharmacoeconomic implications of the widespread use of the polypill, particularly in low- and middle-income countries where the burden of cardiovascular disease is increasing. Although the polypill demonstrates a favorable profile in improving therapeutic compliance and reducing cardiovascular risk factors, debates persist regarding its efficacy compared to individualized treatment regimens. This review summarizes the current evidence on the efficacy, safety, and cost-effectiveness of the polypill in CVD primary and secondary prevention. Furthermore, potential challenges in implementing the polypill strategy include tailoring the components to patient-specific risk profiles and the need for robust evidence from large-scale randomized controlled trials to substantiate its long-term benefits.

Photodynamic Therapy for Oral Squamous Cell Carcinoma: Current Status, Challenges, and Prospects.

Oral squamous cell carcinoma (OSCC) is the most prevalent and deadly malignancy of the head and neck. The standard treatments for OSCC are surgery, radiotherapy, and chemoradiotherapy, which can cause severe cosmetic and functional damage to the oral cavity and impair the patients' quality of life. Photodynamic therapy (PDT) is a promising alternative that uses light-activated photosensitizers to induce selective phototoxicity and necrosis in the target tissues. PDT has several advantages over conventional treatments, such as minimal invasion, low side effects, high selectivity and preservation of the oral function and appearance. This review explores the principles, mechanisms, and current applications of PDT for OSCC. We address the challenges, such as the depth of light penetration and tissue hypoxia, and underscore the progressive innovations in photosensitizer enhancement, nanotechnological integration, and precision therapy. The exploration of biomarkers for refining patient selection and tailoring individualized treatment regimens is also undertaken. PDT holds promise as a secure and efficacious modality for OSCC management. Nonetheless, additional investigation is imperative to refine treatment protocols and validate sustained therapeutic success.

Study the Occurrence of Mycobacterium Bovis in Tuberculosis of Peripheral Lymph Nodes and its Effect on the Course of the Disease.

The incidence of tuberculosis caused by Mycobacterium bovis, not only the pulmonary form, but also the form developing in the extrapulmonary organs, is also increasing from year to year. Despite the large number of EPTB, TBPLN occupies a leading place among diseases of this type and the study of its pathogenic strains is an urgent task in ensuring the effectiveness of treatment. In this regard, the main purpose of the presented manuscript is to determine the frequency of M. bovis in TBPLN, its effect on the development and course of the disease, as well as the effectiveness of treatment. For this purpose, for the first time, the features of education that occur in patients in peripheral lymph nodes using instrumental methods of ultrasound, computed tomography, and magnetic resonance imaging have been identified. In subsequent studies, 110 patients with peripheral lymph node pathology were diagnosed with TBPLN by detecting mycobacteria in pathological material using general hematological, microbiological and gene-molecular (Gene Xpert) methods. In order to ensure the high effectiveness of drugs used for medicinal purposes, strains of the pathogen were detected using histological, cytological studies, BCG test and specific analyzes such as Diaskintest, Quantiferon test, immunological tests. The study showed that about 80% of patients had M bovis in the overall assessment, 76.4% of patients were sensitive to rifampicin, 9.1% of patients had rifampicin-resistant bacteria, and 14.5% of patients did not have mycobacteria. Therapeutic measures were carried out in 2 different modes, such as standard and individual or with replacement, when all patients were divided into 2 groups. During the period from the 56-day intensive phase of standard treatment to the 84-day intensive phase, a total of 40 patients had a sharp decrease in lymph nodes, elimination of purulent inflammation, and after a while 22 patients in this group had a relapse. In the individual treatment regimen, Levofloxacin and linezolid were used instead of pyrazinamide. While the effectiveness of treatment was achieved in 48 patients of group II after 56 and 84 days of the intensive phase, relapses after a certain time were observed in only 6 patients. When choosing an individual treatment regimen in patients diagnosed with M. bovis, a decrease in relapses to 11.5% is achieved. When M. bovis is detected, an individual scheme of antibacterial treatment of tuberculosis is selected, in which, instead of pyrazinamide, it is recommended to choose one of the reserve lines, depending on the sensitivity of the pathogen to drugs.

Artificial Intelligence in Cancer: A SWOT Analysis

Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.

CLAG±DAC regimen in the treatment of refractory/relapsed acute myeloid leukemia

Objective: To investigate the efficacy and prognosis of CLAG±DAC (Clofarabine, Cytarabine, G-CSF±Decitabine) chemotherapy in patients with relapsed/refractory acute myeloid leukemia (R/R AML) . Methods: Continuous cases of R/R AML treated with the CLAG+DAC protocol or CLAG alone at the First Affiliated Hospital of Soochow University from January 2017 to December 2021 were retrospectively analyzed. The baseline characteristics, individual treatment regimen, treatment effect, disease progression, and survival status of patients were recorded. The factors influencing the efficacy of the CLAG±DAC chemotherapy regimens were analyzed, and the overall survival (OS) time after reinduction was calculated using the Kaplan-Meier method. Results: This study included a total of 53 patients, with 33 male patients and an average age of 40.6 years. Thirty-three patients achieved complete remission (CR+CRi) of the disease after the CLAG±DAC chemotherapy regimen and six patients achieved partial remission (PR), while 14 did not. Thirty-two patients eventually underwent hematopoietic stem cell transplantation, and the median OS of the patients was 55.9 months until follow-up. Patients with disease remission after the application of the CLAG±DAC chemotherapy had a significantly longer survival time than those without remission (P<0.001). The results of the multifactorial analysis have revealed that combined DAC (OR=4.60, 95% CI 1.14-23.5, P=0.04) and DNMT3A mutation (OR=0.14, 95% CI 0.01-0.89, P=0.05) were the factors influencing the efficacy of the CLAG±DAC chemotherapy regimen. The remission rate was relatively higher in patients with R/R AML combined with FLT3-ITD mutation by applying the DAC+CLAG regimen (OR=10.84, 95%CI 1.48-288.50, P=0.04) . Conclusion: The CLAG±DAC regimen is considered effective in patients with R/R AML, whereas decitabine combined with the CLAG regimen is more suitable for patients with R/R AML combined with FLT3-ITD mutation.

MicroRNAs implicated in canine diffuse large B-cell lymphoma prognosis.

Diffuse large B-cell lymphoma (DLBCL) is the most prevalent subtype of non-Hodgkin lymphoma (NHL) in domestic dogs, with many similarities to its human counterpart. The progression of the disease is rapid, and treatment must be initiated early to achieve cancer remission and extend life. This study examined the relationship between progression-free survival (PFS) and microRNA (miRNA) expression in dogs with DLBCL. miRNAs are small non-coding RNA molecules that typically regulate gene expression post-transcriptionally. They are involved in several pathophysiological processes, including the growth and progression of cancer. Based on the analysis of small RNA sequencing (sRNA-seq) data, we validated a group of miRNAs in lymph nodes from 44 DLBCL-affected dogs with known outcomes. We used quantitative PCR to quantify their expression and report a specific subset of miRNAs is associated with decreased PFS in dogs with DLBCL. The miR-192-5p and miR-16-5p expression were significantly downregulated in dogs with increased PFS. These results indicate that miRNA profiling may potentially identify dogs with DLBCL that will experience poor outcomes following treatment. Identifying specific miRNAs that correlate with the progression of canine DLBCL could aid the development of individualized treatment regimens for dogs.

Serum biomarkers for predicting microvascular complications of diabetes mellitus.

Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients. Identifying reliable biomarkers promptly is pivotal for early detection and intervention in these severe complications. This review offers a thorough examination of the latest research concerning serum biomarkers for the prediction and assessment of diabetic microvascular complications. It encompasses biomarkers associated with glycation, oxidative stress, inflammation, endothelial dysfunction, basement membrane thickening, angiogenesis, and thrombosis. The review also highlights the potential of emerging biomarkers, such as microRNAs and long non-coding RNAs. Serum biomarkers are emerging as valuable tools for the early assessment and therapeutic guidance of diabetic microvascular complications. The biomarkers identified not only reflect the underlying pathophysiology but also align with the extent of the disease. However, further validation across diverse populations and improvement of the practicality of these biomarkers in routine clinical practice are necessary. Pursuing these objectives is essential to advance early diagnosis, risk assessment, and individualized treatment regimens for those affected by diabetes.

Hypofractionated stereotactic radiotherapy for brain metastases in lung cancer patients: dose‒response effect and toxicity

BackgroundLung cancer is a common cause of brain metastases, approximately 40% of patients with lung cancer will develop brain metastases at some point during their disease. Hypofractionated stereotactic radiotherapy (HSRT) has been demonstrated to be effective in controlling limited brain metastases. However, there is still no conclusive on the optimal segmentation of HSRT. The aim of our study was to explore the correlation between the HSRT dosage and its treatment effect and toxicity.MethodsA retrospective analysis was conducted on patients with non-small cell lung cancer (NSCLC) brain metastasis at Hangzhou Cancer Hospital from 1 January 2019 to 1 January 2021. The number of brain metastases did not exceed 10 in all patients and the number of fractions of HSRT was 5. The prescription dose ranges from 25 to 40 Gy. The Kaplan–Meier method was used for estimation of the localised intracranial control rate (iLC). Adverse radiation effects (AREs) were evaluated according to CTCAE 5.0. This study was approved by the Institutional Ethics Review Board of the Hangzhou Cancer Hospital (#73/HZCH-2022).ResultsForty eligible patients with a total of 70 brain metastases were included in this study. The 1-year iLC was 76% and 89% in the prescribed dose ≤ 30 Gy and > 30 Gy group, respectively (P < 0.05). For patients treated with HSRT combined with targeted therapy, immunotherapy and chemotherapy, the 1-year iLC was 89%, 100%, and 45%, respectively. No significant associations were observed between the number, maximum diameter, location, and type of pathology of brain metastases. The rate of all-grade AREs was 33%. Two patients who received a total dose of 40 Gy developed grade 3 headache, the rest of the AREs were grade 1–2.ConclusionsIncreasing the prescription dose of HSRT improves treatment effect but may also exacerbate the side effects. Systemic therapy might impact the iLC rate, and individualized treatment regimens need to be developed.

Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data.

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.

Natural deep eutectic solvent for the simultaneous derivatization and microextraction of isoniazid from human plasma

BackgroundPersonalized medicine is a rapidly revolving field that offers new opportunities for tailoring disease treatment to individual patients. The main idea behind this approach is to carefully select safe and effective medications and treatment plant based on each patient's unique pharmacokinetic profile. Isoniazid is a first-line anti-tuberculosis drug that has interindividual variability in its metabolic processing, leading to significant differences in plasma concentrations among patients receiving equivalent doses. This variability necessitates the creation of individualized treatment regimens as part of personalized medicine to achieve more effective therapy. ResultsIn this work, a deep eutectic solvent-based liquid-liquid microextraction approach for the separation and determination of isoniazid in human plasma by high-performance liquid chromatography with UV–Vis detection was developed for the first time. A new natural deep eutectic solvent based on thymol as a hydrogen bond donor and 4-methoxybenzaldehyde as a hydrogen bond acceptor was proposed as the extraction solvent. The developed microextraction procedure assumed two simultaneous processes during the mixing of the sample and extraction solvent: the derivatization of the polar analyte in the presence of 4-methoxybenzaldehyde (component of the natural deep eutectic solvent) with the formation of a hydrophobic Schiff base (1); mass transfer of the Schiff base from the sample phase to the extraction solvent phase (2). Under optimal conditions, the limits of detection and quantification were 20 and 60 μg L−1, respectively. The RSD value was <10 %, the extraction recovery was 95 %. SignificanceIn this work, the possibility of isoniazid derivatization in the natural deep eutectic solvent phase with the formation of the Schiff base was presented for the first time. The approach provided the separation and preconcentration of polar isoniazid without the use of expensive derivatization agents and solid-phase extraction cartridges. The formation of the Schiff base was confirmed by mass spectrometry.

Robust Covariate-Balancing Method in Learning Optimal Individualized Treatment Regimes

Summary One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, in which an outcome regression model or a propensity score model usually needs to be assumed for most of the existing statistical methods. However, if either model assumption is invalid, the estimated treatment regime is not reliable. In this article, we first define a contrast value function, which is the basis of the study for individualized treatment regimes. Then we construct a hybrid estimator of the contrast value function, by combining two types of estimation methods. We further propose a robust covariate-balancing estimator of the contrast value function by combining the inverse probability weighted method and matching method, which is based on the covariate balancing propensity score proposed by Imai and Ratkovic (2014). Theoretical results show that the proposed estimator is doubly robust, that is, it is consistent if either the propensity score model or the matching is correct. Based on a large number of simulation studies, we demonstrate that the proposed estimator outperforms existing methods. Lastly, the proposed method is illustrated through analysis of the SUPPORT study.

AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture.

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation and treatment. In the context of healthcare, artificial intelligence (AI) uses sophisticated computational methods to evaluate, decipher and draw conclusions from patient data. AI has the potential to revolutionize the healthcare industry in several ways, including better managerial effectiveness, individualized treatment regimens and diagnostic improvements. In this research, the ECG signals are preprocessed for noise elimination and heartbeat segmentation. Multi-feature extraction is employed to extract features from preprocessed data, and an optimization technique is used to choose the most feasible features. The i-AlexNet classifier, which is an improved version of the AlexNet model, is used to classify between normal and anomalous signals. For experimental evaluation, the proposed approach is applied to PTB and MIT_BIH databases, and it is observed that the suggested method achieves a higher accuracy of 98.8% compared to other works in the literature.

Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review

This comprehensive study delves into the transformative role of artificial intelligence (AI) and deep learning (DL) in the realm of prostate cancer care, an issue of paramount importance in men’s health worldwide. Prostate cancer, marked by the unchecked growth of cells in the prostate gland, poses risks of tumor formation and eventual metastasis. The crux of combating this disease lies in its early detection and precise diagnosis, for which traditional screening methodologies like Prostate-Specific Antigen (PSA) tests and multiparametric Magnetic Resonance Imaging (mp-MRI) are fundamental. The introduction of AI and DL into these diagnostic avenues has been nothing short of revolutionary, enhancing the precision of medical imaging and significantly reducing the rates of unnecessary biopsies. The advancements in DL, particularly through the use of convolutional neural networks (CNNs) and the application of multiparametric MRI, have been instrumental in improving the accuracy of diagnoses, foreseeing the progression of the disease, and tailoring individualized treatment regimens. This paper meticulously examines various DL models and their successful application in the detection, classification, and segmentation of prostate cancer, establishing their superiority over conventional diagnostic techniques. Despite the promising horizon these technologies offer, their implementation is not without challenges. The requisite for specialized expertise to handle these advanced tools and the ethical dilemmas they present, such as data privacy and potential biases, are significant hurdles. Nevertheless, the potential of AI and DL to inaugurate a new chapter in prostate cancer management is undeniable. The emphasis on interdisciplinary collaboration among scientists, clinicians, and technologists is crucial for pushing the boundaries of current research and clinical practice, ensuring the ethical deployment of AI and DL technologies. This collaborative effort is vital for realizing the full potential of these innovations in providing more accurate, efficient, and patient-centric care in the fight against prostate cancer, heralding a future where the burden of this disease is significantly mitigated.

Effectiveness and safety analysis of serplulimab-based regimens in patients with HER2-negative advanced gastric cancer from a real-world retrospective study.

e16058 Background: Immune checkpoint inhibitors (ICIs) combined with chemotherapy are currently the preferred regimen for the treatment of HER-2 negative advanced gastric cancer. Serplulimab, an anti-PD-1 inhibitor, has been conditionally approved for the treatment of adult advanced gastric cancer. Because of the diverse nature of patients (pts), patient-centered, individualized treatment require accumulation of experience for real-world applications. Methods: We retrospectively studied 42 HER2-negative advanced gastric adenocarcinoma pts whose safety profiles of regimen containing serplulimab could be evaluated from April 2022 to October 2023 in the First Affiliated Hospital of Soochow University. Individualized treatment regimens mainly included serplulimab combined with one or two selected drug(s) from platinum, taxanes, and fluoropyrimidines. Results: Cut-off on January 30th, 2024, 30 pts (71.4%) received serplulimab based regimens as the first line (1L), 12 pts (28.6%) received the second or more line (≥2L) therapy. The characteristics were as follows: the median age, 66 years (range 35-80 years); male/female 33 (78.6%)/9(21.4%); ECOG performance status 0-1/38, ≥2/4. The median follow-up was 5.7 months (range 0.2-22.3 mos). A total of 227 cycles of treatment was administrated with a median of 4 cycles (range 1-17). Among the pts evaluable for RECIST 1.1, ORR and DCR were 42.9% and 69.0% respectively. There were 3 pts with 3 or more metastatic lesions achieved PR. Nine 1L pts and one 2L pt receiving serplulimab combined with dual chemotherapy containing platinum achieved disease control more than a year. We also tracked 11 pts who experienced grade 1-2 immune-related adverse events (irAEs) reaped over 6 months PFS. Two of four pts who stopped immunotherapy but maintained 1 or 2 cycles of original combined chemotherapy obtained PFS for more than a year. Median PFS and DoR have not reached. The most common grade 3/4 AEs was myelosuppression (7.1%), increased aminotransferases (4.7%) and elevated creatinine (2.3%). 15 (35.7%) pts experienced immune-related adverse events (irAEs) of any grade including aminotransferase elevation, increased creatinine, but no grade 3 or higher irAEs observed. Conclusions: The real-world data analysis shows that serplulimab-based flexibly combination according to patient status, achieving tumor control in diverse pts while ensuring drug safety. We successfully validated the effectiveness and safety of chemo-immunotherapy strategy in real-world practice. [Table: see text]

Trends in mortality from secondary malignancy in the United States, 1999-2019.

11166 Background: Recent advancements in cancer diagnosis, treatment, and supportive care increased the survivorship of cancer patients. However, the risk of secondary malignancy (SM), associated most commonly with side effects of radiation and chemotherapies, has emerged as a looming threat to childhood and adult-onset cancer survivors. Recognizing the recent trends in malignancy-related deaths and the risk factors associated could assist healthcare professionals and policymakers in constructing appropriate guidelines for timely diagnosis and individual patient-dependent treatment regimens. Hence, our CDC analysis focuses on highlighting trends in mortality from SM from “1999 to 2019”. Methods: Using the deidentified death certificate data from the CDC WONDER database, we analyzed mortality trends from 1999 to 2019 for SM. Age-adjusted mortality rates (AAMRs) per 100,000 people were calculated for the total population, stratified by gender, race, age, and site of SM. Annual percent change (APC) was calculated using the Joinpoint regression software. Results: A total of 1,499,108 deaths occurred from 1999 to 2019 in the US. The overall AAMR showed a decrease from 27.5 in 1999 to 18.83 in 2007 (APC: -4.71), remained at a relatively constant level of 19.11 till 2013 (APC: 0.48), followed by a rapid rise to 23.29 in 2017 (APC: 5.37), and finally a gradual rise to 24.29 in 2019 (APC: 1.97). Men showed consistently higher AAMR than women throughout the study period between 1999 (AAMR Men: 33.31 vs Women: 23.78) and 2019 (AAMR Men: 28.03 vs Women: 21.51). Moreover, Black (AAMR: 23.89) showed the highest mortality rate, followed by Whites (AAMR: 21.46). American Indian (AAMR: 13.93), Asian (AAMR: 13.89), and Hispanic (AAMR: 15.9) showing similar mortality trends. Furthermore, AAMR increased with age, showing the highest mortality rates in patients 75+ yrs (148.3), followed by 55-74 yrs (61.6), 35-54 yrs (9.23), 15-34 yrs (0.7), and ≤ 14 yrs (0.19). More recently the greatest rise in SM from 2013-2019 was observed in adrenal glands (APC: 11.8), bone and bone marrow (APC: 8.7), kidney and renal pelvis (APC: 7.6), and small intestine (APC: 6.9). Less prominent rise was seen in all other types of SM. Conclusions: Despite recent advances in cancer treatments, SM remains one of the most common causes of death in cancer survivors. Our analysis shows that age, gender, race, and site are important factors influencing the risk of SM-related mortality. Although the risk of SM varies across the spectrum of age, findings from one age group could be translated into preventive and screening strategies across other age groups. Guidelines for survivors should be taken into account based on prior treated malignancy and the risk of future ones. Large population based studies are imperative to understand factors behind the recent rise in SM-related mortalities from 2013 onwards.

Risk-aware restricted outcome learning for individualized treatment regimes of schizophrenia

Risk-aware restricted outcome learning for individualized treatment regimes of schizophrenia

Integrated Diagnosis, Treatment and Prognosis in Healthcare using Artificial Intelligence

Artificial Intelligence (AI) has revolutionized healthcare by integrating treatment, diagnosis, and prognosis into a cohesive and patient-centric approach. This study examines how utilising AI technology in healthcare might improve patient management and have a transformational impact. Huge volumes of patient data, including as genetic data, medical records, and treatment outcomes are analysed by AI algorithms, allowing for the creation of individualised treatment regimens based on precise prognostic assessments and diagnoses. Utilising AI-driven decision-making promotes proactive and preventative actions, improving healthcare outcomes. To ensure ethical AI adoption, however, concerns about data privacy, algorithmic bias, and ethical issues must be addressed. In order to demonstrate how AI-driven therapy approaches are successful, case examples are reviewed in this article, demonstrating how they might potentially enhance patient care. As AI develops, its seamless integration with healthcare systems has enormous promise for revolutionising medical practise. It will usher in a new era of accurate, effective, and data-driven patient management, which will ultimately be advantageous to both patients and healthcare professionals.its capacity to enhance patient care.