Exploratory investigation of virtual lesions in gastrointestinal endoscopy using a novel phase-shift method for three-dimensional shape measurement.

Accurate measurement of the size of lesions or distances between any two points during endoscopic examination of the gastrointestinal tract is difficult owing to the fisheye lens used in endoscopy. To overcome this issue, we developed a phase-shift method to measure three-dimensional (3D) data on a curved surface, which we present herein. Our system allows the creation of 3D shapes on a curved surface by the phase-shift method using a stripe pattern projected from a small projecting device to an object. For evaluation, 88 measurement points were inserted in porcine stomach tissue, attached to a half-pipe jig, with an inner radius of 21 mm. The accuracy and precision of the measurement data for our shape measurement system were compared with the data obtained using an Olympus STM6 measurement microscope. The accuracy of the path length of a simulated protruded lesion was evaluated using a plaster model of the curved stomach and graph paper. The difference in height measures between the measurement microscope and measurement system data was 0.24 mm for the 88 measurement points on the curved surface of the porcine stomach. The error in the path length measurement for a lesion on an underlying curved surface was <1% for a 10-mm lesion. The software was developed for the automated calculation of the major and minor diameters of each lesion. The accuracy of our measurement system could improve the accuracy of determining the size of lesions, whether protruded or depressed, regardless of the curvature of the underlying surface.

Efectividad del entrenamiento muscular pélvico temprano en la fuerza de suelo pélvico, síntomas de incontinencia urinaria, función sexual y calidad de vida en pacientes posprostatectomía radical: revisión sistemática de ensayos clínicos aleatorizados / Effectiveness of early pelvic muscle training on pelvic floor strength, urinary incontinence symptoms, sexual function, and quality of life in post-radical prostatectomy patients: Systematic review of randomized clinical trials

Este estudio tuvo como objetivo evaluar la efectividad del entrenamiento muscular pélvico temprano para reducir los síntomas de incontinencia urinaria, mejorar la calidad de vida, función sexual y aumentar la fuerza de suelo pélvico en pacientes posprostatectomía radical. Se realizó una búsqueda en 8 bases de datos hasta el 26 de octubre de 2022, se evaluó la calidad metodológica y el riesgo de sesgo de 14 estudios incluidos (n=1236), se calculó la evidencia y el metaanálisis. El entrenamiento redujo significativamente los síntomas de incontinencia urinaria en comparación con un grupo control (DME=−2,80; IC 95%=−5,21 a −0,39; p=0,02), con heterogeneidad significativa (I2=83%; p=<0,0001) y evidencia moderada. Además, presentó evidencia moderada para mejorar la calidad de vida, y muy baja para mejorar la función sexual y fuerza de suelo pélvico. Estos resultados deben ser observados con precaución debido a la heterogeneidad significativa de los estudios analizados. (AU)

This study aimed to evaluate the effectiveness of early pelvic muscle training in reducing urinary incontinence symptoms, improving quality of life, sexual function, and increasing pelvic floor strength in post-radical prostatectomy patients. A search was carried out in 8 databases until October 26, 2022, the methodological quality and the risk of bias of 14 included studies (n=1236) were evaluated, moreover, the evidence and the meta-analysis were calculated. The intervention significantly reduced urinary incontinence symptoms compared to a control group (SMD=−2.80, 95% CI=−5.21 to −0.39, P=.02), with significant heterogeneity (I2=83%; P=<.0001) and moderate evidence. In addition, it presented moderate evidence to improve quality of life, and very low evidence to improve sexual function and pelvic floor strength. These results should be viewed with caution due to the significant heterogeneity of the studies analysed. (AU)

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Research progress in endometriosis-associated ovarian cancer.

Endometriosis-associated ovarian cancer (EAOC) is a unique subtype of ovarian malignant tumor originating from endometriosis (EMS) malignant transformation, which has gradually become one of the hot topics in clinical and basic research in recent years. According to clinicopathological and epidemiological findings, precancerous lesions of ovarian clear cell carcinoma (OCCC) and ovarian endometrioid carcinoma (OEC) are considered as EMS. Given the large number of patients with endometriosis and its long time window for malignant transformation, sufficient attention should be paid to EAOC. At present, the pathogenesis of EAOC has not been clarified, no reliable biomarkers have been found in the diagnosis, and there is still a lack of basis and targets for stratified management and precise treatment in the treatment. At the same time, due to the long medical history of patients, the fast growth rate of cancer cells, and the possibility of eliminating the earliest endometriosis-associated ovarian cancer, it is difficult to find the corresponding histological evidence. As a result, few patients are finally diagnosed with EAOC, which increases the difficulty of in-depth study of EAOC. This article reviews the epidemiology, pathogenesis, risk factors, clinical diagnosis, new treatment strategies and prognosis of endometriosis-associated ovarian cancer, and prospects the future direction of basic research and clinical transformation, in order to achieve stratified management and personalized treatment of ovarian cancer patients.

CTLA4 expression profiles and their association with clinical outcomes of breast cancer: a systemic review.

Purpose: The cytotoxic T-lymphocyte-associated protein 4 (CTLA4) is involved in the progression of various cancers, but its biological roles in breast cancer (BRCA) remain unclear. Therefore, we performed a systematic multiomic analysis to expound on the prognostic value and underlying mechanism of CTLA4 in BRCA. Methods: We assessed the effect of CTLA4 expression on BRCA using a variety of bioinformatics platforms, including Oncomine, GEPIA, UALCAN, PrognoScan database, Kaplan-Meier plotter, and R2: Kaplan-Meier scanner. Results: CTLA4 was highly expressed in BRCA tumor tissue compared to normal tissue (P < 0.01). The CTLA4 messenger RNA levels in BRCA based on BRCA subtypes of Luminal, human epidermal growth factor receptor 2, and triple-negative BRCA were considerably higher than in normal tissues (P < 0.001). However, the overexpression of CTLA4 was associated with a better prognosis in BRCA (P < 0.001) and was correlated with clinicopathological characteristics including age, T stage, estrogen receptors, progesterone receptors, and prediction analysis of microarray 50 (P < 0.01). The infiltration of multiple immune cells was associated with increased CTLA4 expression in BRCA (P < 0.001). CTLA4 was highly enriched in antigen binding, immunoglobulin complexes, lymphocyte-mediated immunity, and cytokine-cytokine receptor interaction. Conclusion: This study provides suggestive evidence of the prognostic role of CTLA4 in BRCA, which may be a therapeutic target for BRCA. Furthermore, CTLA4 may influence BRCA prognosis through antigen binding, immunoglobulin complexes, lymphocyte-mediated immunity, and cytokine-cytokine receptor interaction. These findings help us understand how CTLA4 plays a role in BRCA and set the stage for more research.

Designing a virtual breast cancer prevention program for Iranian women: A study protocol.

BACKGROUND: The growing number of breast cancer patients in Iran, following the lower referrals of women to screening centers after the outbreak of the COVID-19, suggests the need for designing virtual educational interventions to teach self-care methods to women. The aim of this study is to design a virtual training program for the prevention of breast cancer in women based on the steps of the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) educational design model. MATERIALS AND METHODS: This developmental study will be conducted based on the steps of the ADDIE model. In the first step (analysis), a qualitative study, literature review, and a panel of experts will be conducted to analyze the situation (learners, content, platforms, and media for the electronic presentation of the program). In the design step, the learning objectives, educational strategies, and the way of program's implementation and evaluation will be specified. In the third step, not only are the content, storyboard, and educational program developed, but the pilot study is also conducted and formative assessment is performed. In the fourth step, the program will be provided to the audience and will be implemented as a preliminary program. In the final step, the final virtual education program for the prevention of breast cancer in women will be presented based on the results of the evaluation. CONCLUSIONS: Using a comprehensive and systematic educational design model can be a step toward making changes and encouraging innovations in breast cancer prevention education programs in women based on virtual education. Given the existing needs and conditions, this program can promote cancer preventive behaviors as much as possible, reduce the costs imposed on the family and healthcare systems, and lower the complications and mortality rate caused by the delayed diagnosis of the disease.

Psychological outcomes in patients with rare cancers: a systematic review and meta-analysis.

Background: Rare cancers are those that exhibit an incidence of less than six per 100,000 in a year. On average, the five-year relative survival for patients with rare cancers is worse than those with common cancers. The traumatic experience of cancer can be further intensified in patients with rare cancers due to the limited clinical evidence and the lack of empirical evidence for informed decision-making. With rare cancers cumulatively accounting for up to 25% of all cancers, coupled with the rising burden of rare cancers on societies globally, it is necessary to determine the psychological outcomes of patients with rare cancers. Methods: This PRISMA-adherent systematic review (PROSPERO: CRD42023475748) involved a systematic search of PubMed, Embase, Cochrane and PsycINFO for all peer-reviewed English language studies published since 2000 to 30th January 2024 that evaluated the prevalence, incidence and risk of depression, anxiety, suicide, and post-traumatic stress disorder (PTSD) in patients with rare cancers. Two independent reviewers appraised and extracted the summary data from published studies. Random effects meta-analyses and meta-regression were used for primary analysis. Findings: We included 32 studies with 57,470 patients with rare cancers. Meta-analyses indicated a statistically significant increased risk-ratio (RR) of depression (RR = 2.61, 95% CI: 1.43-4.77, I2 = 97%) and anxiety (RR = 2.66, 95% CI: 1.27-5.55, I2 = 92%) in patients with rare cancers compared to healthy controls. We identified a high suicide incidence (315 per 100,000 person-years, 95% CI: 162-609, I2 = 95%), prevalence of depression (17%, 95% CI: 14-22, I2 = 88%), anxiety (20%, 95% CI: 15-25, I2 = 96%) and PTSD (18%, 95% CI: 9-32, I2 = 25%). When compared to patients with common cancer types, suicide incidence, and PTSD prevalence were significantly higher in patients with rare cancers. Systematic review found that having advanced disease, chemotherapy treatment, lower income, and social status were risk factors for negative psychological outcomes. Interpretation: We highlight the need for early identification of psychological maladjustment in patients with rare cancers. Additionally, studies to identify effective interventions are imperative. Funding: This study was supported by the National Medical Research Council Transition Award, SingHealth Duke-NUS Oncology Academic Clinical Programme, the Khoo Pilot Collaborative Award, the National Medical Research Council Clinician Scientist-Individual Research Grant-New Investigator Grant, the Terry Fox Grant and the Khoo Bridge Funding Award.

Factors influencing self-regulatory fatigue in patients undergoing chemotherapy for gynecologic cancer: a cross-sectional study.

Objective: To understand the current status of self-regulatory fatigue among gynecologic cancer chemotherapy patients and explore influencing factors. Methods: Using convenient sampling, a total of 232 gynecological cancer chemotherapy patients from two tertiary hospitals in Zhengzhou, Henan, China, were selected as study subjects from February 2023 to April 2023. General information questionnaire, Self-Regulatory Fatigue Scale (SRF-S), Strategies Used by People to Promote Health (SUPPH) Scale, Connor-Davidson resilience scale (CD-RISC) and Perceived Social Support Scale (PSSS) were employed for data collection. The data were analyzed using SPSS 26.0 software. Chi-square test and binary logistic regression were executed to explore the correlates of self-regulatory fatigue, the significance level (α) was set at 0.05. Results: The self-regulatory fatigue score of the 232 patients was 44 (36, 56). Binary logistic regression analyses revealed significant associations, demonstrating that residing in urban areas (OR=0.241, P=0.015), having no comorbidities (OR=0.158, P=0.015), increased perceived social support (OR=0.937, P=0.001), strong self-efficacy (OR=0.959, P=0.021), and heightened psychological resilience (OR=0.895, P<0.001) acted as protective factors against self-regulatory fatigue (P < 0.05). Conclusion: Patients residing in rural areas, having more than two comorbidities, lower self-efficacy and psychological resilience levels, and lower perceived social support are indicative of higher levels of self-regulatory fatigue. Identifying these influencing factors can provide references and support for developing individualized support and intervention measures to improve patients' physical and mental well-being.

Targeting NKG2D ligands in glioblastoma with a bispecific T-cell engager is augmented with conventional therapy and enhances oncolytic virotherapy of glioma stem-like cells.

BACKGROUND: Glioblastoma (GBM) almost invariably becomes resistant towards conventional treatment of radiotherapy and temozolomide (TMZ) chemotherapy, partly due to subpopulations of intrinsically resistant glioma stem-like cells (GSC). The oncolytic herpes simplex virus-1 G207 is a promising approach for GBM virotherapy although its efficacy in patients with GBM is often limited. Natural killer group 2 member D ligands (NKG2DLs) are minimally expressed by healthy cells but are upregulated by the DNA damage response (DDR) and in malignant cells with chronic DDR signaling, resulting in innate immune activation. METHODS: We have designed a bispecific T-cell engager (BiTE) capable of cross-linking CD3 on T cells with NKG2DL-expressing GBM cells. We then engineered the G207 virus to express the NKG2D BiTE and secrete it from infected cells. The efficacy of the free BiTE and BiTE delivered by G207 was evaluated in combination with conventional therapies in GBM cells and against patient-derived GSCs in the context of T-cell activation and target cell viability. RESULTS: NKG2D BiTE-mediated cross-linking of GBM cells and T cells causes antigen-independent T-cell activation, pro-inflammatory cytokine release, and tumor cell death, thereby combining direct viral oncolysis with BiTE-mediated cytotoxicity. Surface NKG2DL expression was further elevated on GBM cells following pretreatment with sublethal doses of TMZ and radiation to induce the DDR, increasing sensitivity towards G207-NKG2D BiTE and achieving synergistic cytotoxicity. We also demonstrate a novel strategy for targeting GSCs that are non-permissive to G207 infection but remain sensitive to NKG2D BiTE. CONCLUSIONS: We propose a potential model for targeting GSCs in heterogeneous tumors, whereby differentiated GBM cells infected with G207-NKG2D BiTE produce NKG2D BiTE locally, directing T-cell cytotoxicity towards the GSC subpopulations in the tumor microenvironment.

Primary Human Papillomavirus Test Uptake and Cervical Cancer Screening Trends in the Midwest, United States.

INTRODUCTION/OBJECTIVES: Despite U.S. Preventive Services Task Force and American Cancer Society endorsement of primary HPV screening, limited published data shows low uptake. PRIMARY AIM: Assess cervical cancer screening rates over time, particularly primary HPV test uptake, among patients in a midwestern practice. SECONDARY AIM: Evaluate associations between sociodemographics and screening adherence. METHODS: Cross-sectional study. Qualifying subjects and type of screening test used were identified by applying ICD-9, ICD-10, lab test, and CPT codes to the Unified Data Platform. Sociodemographics were found through the electronic health record. RESULTS: Primary HPV uptake represented <1% of annual screening from 1/2017 to 1/2022. On 1/1/2022, only 55% of 21 to 29 year old and 63% of 30 to 65 year old were up to date with screening among the studied population. For 21 to 29 year old, compared with White women, Black women were 28% less likely to be screened [RR = 0.72 (0.66-0.79)]. Compared with never-smokers, current smokers were 9% less likely to be screened [RR = 0.91 (0.87-0.96)], past smokers were 14% more likely [RR = 1.14 (1.09-1.2)]. Among 30 to 65 year old, compared with White women, Black women were 14% less likely to be screened [RR = 0.86 (0.81-0.9)]. Compared with never-smokers, current smokers were 21% less likely to be screened [RR = 0.79 (0.77-0.81)], past smokers were 6% less likely [RR = 0.94 (0.92-0.95)]. Jointly considering race, ethnicity, smoking status, Charlson score, and rurality, findings were similar for 21 to 29 year old; Black women were screened less than White women [RR = 0.73 (0.67-0.79)]; current smokers [RR = 0.9 (0.85-0.94)] and past smokers [RR = 1.12 (1.06-1.17)] were screened less than never smokers. For 30 to 65 year old, Black women were screened less than White women [RR = 0.83 (0.79-0.88)]; current smokers [RR = 0.8 (0.78-0.81)] and past smokers [RR = 0.95 (0.93-0.96)] were screened less than never smokers. CONCLUSIONS: Screening rates remained below the Healthy People 2030 goal of 79.2% over time, particularly for younger Black women and current smokers, with minimal use of primary HPV screening.

Time to full weight-bearing with the use of a calcium sulfate-calcium phosphate bone substitute as a bone void filler following extended curettage in the treatment of primary benign bone tumours.

PURPOSE: The primary objective of this study was to determine time to full weight-bearing after the use of a calcium-sulfate-calcium phosphate bone substitute (CaSO4/CaPO4) as a bone void filler in the treatment of primary benign bone tumours following intralesional curettage. The secondary objectives were to determine surgical complications and recurrence rates. METHODS: Retrospective review of patients identified from a surgeon-specific orthopaedic oncology database, who underwent curettage of benign bone tumours and subsequent bone void filling with CaSO4/CaPO4. RESULTS: A total of 39 patients (20 males, 19 females) met inclusion criteria with an average age of 31 years (range: 13 to 62 years), a median follow-up of 3.7 years, and a maximum follow-up of 11 years. The most common tumour diagnosis was giant cell tumour of bone (GCT) (n = 19), and the most common location was the proximal tibia (n = 9). The mean volume of tumour excised was 74.1 cm3 including extraosseous bone expansion due to tumour growth, with a mean of volume of 21.4 mL of CaSO4/CaPO4 used to fill the intraosseous cavitary defects to restore normal bone anatomy. None of the lesions required additional internal fixation. The primary outcome measure, average time to full weight-bearing/full range of motion, was 11 weeks and 6 weeks for upper and lower extremity lesions, respectively. Secondary outcomes included tumour recurrence requiring reoperation in five patients and infection requiring reoperation in two patients. CONCLUSION: This study demonstrates that CaSO4/CaPO4 is a viable option as a bone void filler in the reconstruction of cavitary defects following removal of primary benign bone tumours. CaSO4/CaPO4 provides sufficient bone regeneration early in the post-operative period to allow progression to full weight-bearing within weeks without the need for internal fixation. There were no graft-specific complications noted.

Differential levels of circulating RNAs prior to endometrial cancer diagnosis.

Endometrial cancer (EC) is one of the most common female cancers and there is currently no routine screening strategy for early detection. An altered abundance of circulating microRNAs (miRNAs) and other RNA classes have the potential as early cancer biomarkers. We analyzed circulating RNA levels using small RNA sequencing, targeting RNAs in the size range of 17-47 nucleotides, in EC patients with samples collected prior to diagnosis compared to cancer-free controls. The analysis included 316 cases with samples collected 1-11 years prior to EC diagnosis, and 316 matched controls, both from the Janus Serum Bank cohort in Norway. We identified differentially abundant (DA) miRNAs, isomiRs, and small nuclear RNAs between EC cases and controls. The top EC DA miRNAs were miR-155-5p, miR-200b-3p, miR-589-5p, miR-151a-5p, miR-543, miR-485-5p, miR-625-p, and miR-671-3p. miR-200b-3p was previously reported to be among one of the top miRNAs with higher abundance in EC cases. We observed 47, 41, and 32 DA miRNAs for EC interacting with BMI, smoking status, and physical activity, respectively, including two miRNAs (miR-223-3p and miR-29b-3p) interacting with all three factors. The circulating RNAs are altered and show temporal dynamics prior to EC diagnosis. Notably, DA miRNAs for EC had the lowest q-value 4.39-6.66 years before diagnosis. Enrichment analysis of miRNAs showed that signaling pathways Fc epsilon RI, prolactin, toll-like receptor, and VEGF had the strongest associations.

Early detection of HCC in patients with cirrhosis using AI; a Systematic Review.

Introduction: Hepatocellular carcinoma constitutes for approximately 75% of primary cancers of liver. Around 80- 90% of patients with HCC have cirrhosis at the time of diagnosis. Use of AI has recently gained significance in the field of hepatology, especially for the detection of HCC, owing to its increasing incidence and specific radiological features which have been established for its diagnostic criteria. Objectives: A systematic review was performed to evaluate the current literature for early diagnosis of hepatocellular carcinoma in cirrhotic patients. METHODS: Systematic review was conducted using PRISMA guidelines and the relevant studies were narrated in detail with assessment of quality for each paper. RESULTS: This systematic review displays the significance of AI in early detection and prognosis of HCC with the pressing need for further exploration in this field. CONCLUSIONS: AI can have a significant role in early diagnosis of HCC in cirrhotic patients.

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT). Summarizing recent research findings underscores the significant strides made in the accurate assessment of pCR using AI, including deep learning and radiomics. Such AI-driven models offer promise in optimizing clinical decisions, personalizing treatment strategies, and potentially reducing the burden of unnecessary treatments, thereby improving patient outcomes. Furthermore, the review acknowledges the potential of AI to address healthcare disparities in Low- and Middle-Income Countries (LMICs), where accessible and scalable AI solutions may enhance BC management. Collaboration and international efforts are essential to fully unlock the potential of AI in BC care, offering hope for a more equitable and effective approach to treatment worldwide.

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on dynamic contrastenhanced MRI (DCE-MRI) data to distinguish molecular subtypes of breast cancer (BC). Implications to noninvasive assessment of molecular subtype include reduction in procedure risks, tailored treatment approaches, ability to examine entire lesion, follow-up of tumour biology in response to treatment and evaluation of treatment resistance and failure secondary to tumour heterogeneity. Recent studies leverage radiomics and AI on DCE-MRI data for reliable, non-invasive breast cancer subtype classification. This review recognizes the potential of AI to predict the molecular subtypes of breast cancer non-invasively.

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review.

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases-fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation-the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes- crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs.

Clinical applications of AI-prediction tools in spine surgery: a narrative review.

Spine surgery has grown into a wide, complex field encompassing trauma surgery to deformity to tumours. Artificial intelligence (AI) based technology has been particularly useful in improving imaging-reporting and detection of predictive patterns. The purpose of this narrative review is to present practical approaches towards implementing upcoming AI spine research for clinicians to help improve practices, clinical throughput, and surgical decision-making.

Virtual reality-based interventions, a neoteric approach for management and rehabilitation in patients with breast cancer.

Breast cancer (BC) patients and survivors can experience immense emotional and psychosocial trauma. Treatment modalities for BC, including surgery, chemotherapy and radiotherapy are associated with certain displeasing and undesirable effects, including physical restrictions as well as mental stress. However, it has been ascertained that appropriate supportive and rehabilitative strategies can significantly help to alleviate the distress. Along with several conventional physical therapy options, the novel Virtual Reality (VR) tool has opened a new gateway in rehabilitative approaches in patients with BC. We reviewed the role of VR based management for BC-related incapacitations and found that its efficacy is comparable to that of contemporary therapy options. It has the additional benefits of modulating pain perceptions, improving mobility, and overall enhancing the quality of life of BC survivors.

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific metastasis management, and addressing both surgical and minimally invasive therapies as essential components. Moreover, the transformative role of Artificial Intelligence (AI) is spotlighted. However, while the future looks promising, several limitations need addressing, including the need for further research, especially in diverse patient populations and resource-challenged settings. AI implementation may require overcoming the lack of Electronic Health Records acceptance in resource-challenged countries, which contributes to a scarcity of large datasets for AI training. As AI continues to evolve, validation and regulatory aspects must be continually addressed for seamless integration into clinical practice. In summary, this review outlines the evolving landscape of OMBC management, emphasizing the need for comprehensive research, global collaboration, and innovative AI solutions to enhance patient care and outcomes.

Artificial intelligence- image learning and its applications in neurooncology: a review.

Image learning involves using artificial intelligence (AI) to analyse radiological images. Various machine and deeplearning- based techniques have been employed to process images and extract relevant features. These can later be used to detect tumours early and predict their survival based on their grading and classification. Radiomics is now also used to predict genetic mutations and differentiate between tumour progression and treatment-related side effects. These were once completely dependent on invasive procedures like biopsy and histopathology. The use and feasibility of these techniques are now widely being explored in neurooncology to devise more accurate management plans and limit morbidity and mortality. Hence, the future of oncology lies in the exploration of AI-based image learning techniques, which can be applied to formulate management plans based on less invasive diagnostic techniques, earlier detection of tumours, and prediction of prognosis based on radiomic features. In this review, we discuss some of these applications of image learning in current medical dynamics.