Following the release in 2016 of the report of the American Association of Physicists in Medicine Task Group 100, there has been growing interest in the use of prospective hazard analysis in radiation therapy. System Theoretic Process Analysis (STPA) is an emerging technique in this domain that is particularly suited to processes that involve time sensitive collaboration, decision-making and/or automation.

The goal of this research was to use STPA to evaluate existing processes and procedures with an aim to identify improvements, gaps or unforeseen risks stemming from implementing real-time adaptive treatment on a helical tomotherapy platform.

The Radixact treatment delivery system (Accuray Inc., Sunnyvale, CA, USA), an evolution of the Tomotherapy platform, incorporates upgrades such as the Synchrony system for real-time motion monitoring and treatment adaptation. In collaboration with a team from the radiation oncology department of a large public hospital, a prospective hazard analysis focused on the real-time adaptive capabilities of the Radixact Synchrony system was conducted using STPA. The system boundaries were defined and a control structure model comprising sub-systems and control actions was developed. Unsafe control actions were identified and broad-based causal scenarios were generated. The causal scenarios that were novel, specific to Synchrony or challenging to mitigate were selected for further analysis regarding impacts and potential causes, following which mitigation strategies were proposed, taking into consideration the hierarchy of controls.

A control structure model encompassing the entire patient journey was developed, incorporating all the hardware and software components and human decision makers. The model consisted of 12 sub-systems and 21 control actions, resulting in 108 unsafe control actions and 595 causal scenarios. Sixty-one causal scenarios were selected for further analysis, for which mitigation strategies were proposed based on the hierarchy of controls. These included the development of better reference documentation, the systematic testing of the sensitivity of tracking performance to changes in tracking parameters, guidance around setting and documenting tracking parameters, and documentation review.

STPA was effectively used to assess the Radixact Synchrony system's real-time adaptive radiation therapy capabilities, providing insight into how the system could become unsafe throughout the patient journey. While focused on Radixact Synchrony and real-time adaptive radiation therapy, this study offers a transferable example of STPA application, from analysis initialization to mitigation, that can inform other safety assessments in radiation therapy.

As bariatric surgery becomes increasingly common, reconstructive surgeons are encountering more massive weight loss (MWL) patients requiring breast reconstruction. MWL alters breast anatomy, tissue characteristics, and healing capacity, potentially impacting reconstructive outcomes.

To update and evaluate the evidence on how MWL affects complication and revision rates, flap-relevant anatomy, and patient-reported outcomes (PROMs) following postmastectomy breast reconstruction. In addition, research gaps were identified.

This systematic review updated a previous review and followed PRISMA guidelines. Eligible studies included cohort studies and case series of postmastectomy breast reconstruction after MWL. Data extraction and appraisal were performed independently by two reviewers, with evidence quality rated using the GRADE system.

Fifteen studies met the inclusion criteria, including three case-control studies and twelve case series, reporting outcomes for 102 patients after massive weight loss (MWL). Most reconstructions used abdominally based free flaps, with few MWL-specific modifications. MWL patients experienced significantly higher rates of delayed wound healing, surgical-site infection, fat necrosis, and need for revision compared with controls, while total flap loss rates were similar. Evidence on implant-based reconstruction, vascular anatomy, and PROMs was scarce. The overall certainty of evidence was very low (GRADE ⊕⊝⊝⊝).

Breast reconstruction after MWL is associated with increased wound-healing complications and revision rates, though patient satisfaction appears acceptable. Evidence remains limited by small, heterogeneous studies and a lack of controlled or prospective data. Future research should address optimal reconstructive techniques, timing, and patient selection, including the identification of modifiable risk factors and the use of PROMs to guide evidence-based care.

Pancreatic cancer (PC) ranks among the most lethal malignant neoplasms, primarily due to its late-stage diagnosis and lack of available therapeutic modalities. This study aimed to characterise the current PC epidemiological profiles, lifestyle-related contributors, and projections to unveil its global disease burden.

Using 2022 data from the Global Cancer Observatory (GCO), we estimated PC incidence, mortality, 5-year prevalence, and mortality-incidence-ratio. Modifiable risk factors were extracted from the Global Health Observatory to identify its predictive model. The temporal trends were assessed via estimated annual percentage changes (EAPCs) stratified by age and gender, and the future projection was also collected from GCO, 2022-2050, estimated number of new cases and deaths data.

In 2022, Northern America and Europe had the highest PC burden, with males consistently affected more than females. Alarmingly, a concerning increase in PC mortality was observed among older females. Projections indicate an 85-91% increase in elderly PC cases by 2050, with Asia facing the greatest challenge (487 087 estimated new cases) and Africa being estimated with the fastest mortality growth (159.2%). Strong positive correlations were observed between PC prevalence and human development index (HDI), as well as lifestyle factors e.g. raised total cholesterol (correlation coefficient (r) = 0.695, P < 0.001), cigarette smoking (r = 0.528, P < 0.001), and alcohol drinking (r = 0.505, P < 0.001).

This research underlines the urgent need for region-specific interventions, not only for Northern America and Europe, which currently bear a high PC burden, but also for high-risk populations like Asia and Africa. The projected 85-91% rise in elderly PC cases by 2050, coupled with emerging risks in young females (incidence rose in n/N = 39/52 countries), demands prioritised research on modifiable factors and sex-tailored prevention strategies. These discoveries call for global action to mitigate the escalating PC burden through demographic-targeted public health measures.

CAR T cell therapy is a promising immunotherapy for hematologic malignancies, yet early prediction of outcomes and adverse events remains difficult, especially in small real-world cohorts. We retrospectively analyzed 14 adult patients with diffuse large B-cell lymphoma (DLBCL) treated with CAR T cells in Slovenia, assessing IL-6 increase rates and established predictive metrics including EASIX-C, CAR-HEMATOTOX, and IBPS on Day -5 (pre-lymphodepletion) and Day 0 (infusion). Contrary to our expectation, an inverse correlation was observed between the increase rate of IL-6 and the occurrence of severe cytopenias, indicating higher IL-6 increase rate leads to less severe cytopenias. The EASIX-C score showed higher predictive values when calculated on Day 0, contrary to the CAR-HEMATOTOX score, whose higher predictive values were observed on Day -5. The IBPS predictive values showed mixed results when comparing Day -5 to Day 0. We observed 50% response rate and 29% remission rate. The results highlight the utility of predictive scores, with unexpected findings on IL-6 suggesting further study is necessary.

Financial toxicity is a multidimensional construct shaped by material, psychological, and administrative pressures. This study examines how two components, namely, material burden and a narrow administrative indicator (insurance denial), are associated with cancer survivors' depression symptom severity and self-perceived general health.

We analyzed data from 959 cancer survivors in the Health Information National Trends Survey (HINTS; weighted n = 39,925,127). Material burden was measured by self-reported financial harm due to cancer, and administrative burden by insurance denial. Depression symptom severity was assessed using the Patient Health Questionnaire-4, and general health via self-perceived health. Multivariate logistic and linear regression models evaluated associations between burdens and outcomes.

Nearly half (41.6%) of survivors reported financial harm from cancer, while 6.3% reported insurance denial. Material burden was significantly associated with increased depression symptom severity (odds ratio: 1.33; 95% CI: 1.01-1.70) and poorer self-perceived health (B: -0.208; SE: 0.049; p < 0.001). Insurance denial showed no significant association with either outcome.

Material burden was associated with survivors' mental and physical well-being, underscoring the need for interventions that reduce financial strain. Insurance denial, while not directly associated with outcomes in this study, remains an important indicator of administrative burden. Future work should incorporate broader measures of administrative complexity to clarify its role in financial toxicity and survivorship outcomes.

To construct an efficient predictive model for post-lung cancer resection delirium (POD) using artificial intelligence, with a focus on leveraging synthetic data (generated via the Synthetic Data Vault [SDV] framework) in small-sample training scenarios. Additionally, we aim to enhance clinical interpretability through a locally interpretable model-agnostic explanation (LIME) method, thereby addressing the existing research gap in AI-driven POD prediction following pneumonectomy. The SDV framework was employed to generate 2,000 synthetic data points (serving as the training set), while real-world data from Figshare (n = 570 cases, used as the test set) were utilized to validate the model. Twelve machine learning algorithms (e.g., Gaussian Naive Bayes [GNB] and random forest) were incorporated, with performance metrics including accuracy, recall, and AUC evaluated via 50% cross-validation. LIME was applied to interpret individual sample predictions and analyze the contributions of key features to POD risk In this study, LIME was employed to explain the prediction outcome of a Decision Tree classifier for a single sample in a postoperative delirium dataset. Utilizing machine learning algorithms, the study identified preoperative blood glucose levels, forced expiratory volume (VC), mean corpuscular volume (MCV), and preoperative albumin levels as the four most critical factors influencing delirium. The GNB algorithm exhibited an accuracy of 89.8% within the real-world dataset cohort. In terms of precision, Gaussian Naive Bayes (gnb) ranked first. For recall and F1 score, gnb also performed the best, with a recall rate of 0.263 and an F1 score of 0.256. LinearSVC achieved the highest AUC value of 0.763, followed by Logistic Regression (0.754), MLPC (0.752), and gnb (0.727) in terms of area under the curve. The study demonstrates the effectiveness of using synthetic data for training AI models in predicting postoperative delirium. The findings suggest that GNB could be a preferred algorithm for predicting postoperative delirium.

Breast cancer is the leading cause of cancer death among Brazilian women, yet mortality estimates are often underestimated due to ill-defined causes, incomplete diagnoses, underreporting, and data quality limitations. Using national mortality data from 2000 to 2023, we examined the spatial distribution of breast cancer mortality among women aged 20 years and older, comparing uncorrected and adjusted estimates. Adjustments were applied to correct ill-defined causes, incomplete diagnoses, underreporting, and other data quality limitations using methods developed by the World Health Organization and the Brazilian Institute of Geography and Statistics (IBGE). Age-standardized mortality rates were calculated for five time periods using the World Health Organization (WHO) standard population, and spatial patterns were analyzed using choropleth maps, Moran I, and Local Indicators of Spatial Association (LISA). A total of 328,319 breast cancer deaths were reported, increasing to 385,068 (+ 17.3%) after adjustment. Corrections had the greatest effect in the North and Northeast in 2000-2004 (up to + 69.9%), but declined substantially over time. Mortality remained consistently higher in wealthier regions, while adjustments revealed underestimation in historically underserved areas. These findings reveal enduring geographic inequalities in breast cancer mortality and underscore the urgent need for targeted interventions and improved surveillance systems.

Lung cancer is the third most common cancer in the US with a 5-year survival rate of 17%. Non-small cell lung cancer, especially adenocarcinoma, prevails. Therefore, early detection and biomarker discovery are extremely important. This study uses deep learning to find new biomarkers for lung adenocarcinoma. RNA-Seq data from 522 samples, including 506 lung adenocarcinoma patients and 16 healthy controls, were analyzed. DEGs were identified after strict preprocessing, and deep learning algorithms predicted markers. Functional annotation, pathway, and protein interaction analyses elucidated the biological importance of DEGs. Clinical relevance was assessed by correlation with clinical parameters and survival analysis. External validation was carried out using GDAC and GEO datasets. Blood samples from 30 lung adenocarcinoma patients and 30 healthy people were analyzed by real-time PCR to validate the expression levels of key genes. Among 522 participants(506 cases, 16 controls), the mean age was 62.95 ± 15.71 years. Normalized data showed 3,513 DEGs. The deep learning model had a predictive accuracy of 98.44%, Brier score (probability MSE) = 0.0013, and AUC of 1.0. CYP3A7 had the highest effect size. ROC analysis found diagnostic genes A2M, CYP2C9, and SIRPD (Ensembl ID: 128646) with a sensitivity of 0.96. Real-time PCR showed upregulated CYP2C9, KRT14, and PECAM1 and downregulated A2M in patients compared to controls(P < 0.001). Bioinformatics-identified genes are potential markers for early lung adenocarcinoma detection and management. RT-PCR validation shows AI's effectiveness in identifying biomarkers, enabling prompt treatment to halt disease progression.

Sex cord-stromal tumours (SCSTs) in children are rare gonadal neoplasms exhibiting differentiation towards sex cord stromal elements. This case series explores paediatric SCSTs, presenting clinical, gross, histologic, and immunohistochemical findings, with a focus on limited information available about these tumours in African children. This retrospective study includes 9 cases of ovarian and testicular SCSTs (8 female and 1 male) in patients below 16 years of age. Female patients presented with various SCST subtypes, including juvenile granulosa cell tumours (JGCTs), fibroma, and mixed tumours. Precocious puberty was a common presentation. The male patient had a Leydig cell tumour. Tumour sizes varied, with the smallest in the testis and the largest in a 3-year-old girl. Histologically, most cases exhibited characteristic features of their respective subtypes. Paediatric SCSTs are rare but important considerations in children with gonadal masses. Their incidence in African children is largely unknown. JGCTs are the most prevalent ovarian subtype. Leydig cell tumours are rare in children and can present with virilisation. Mixed SCST-germ cell tumours, though extremely rare, warrant careful consideration and immunohistochemistry for accurate diagnosis.

The prevalence of oncogenic driver mutations in early-stage resectable non-small-cell lung cancer (rNSCLC) in Malaysia remains unknown. This information may guide treatment decisions, especially tyrosine kinase inhibitor (TKI) use. We characterised the genomic landscape of early-stage NSCLC in a surgical cohort and explored its impact on TKI administration, particularly osimertinib.

146 patients who underwent curative resection for early-stage rNSCLC were included in this study. Real-time polymerase chain reaction (PCR) and nextgeneration sequencing (NGS) were used to identify genetic alterations in tumour samples. Associations between EGFR status and clinico-pathological characteristics were analysed using uni- and multivariate logistic regression.

A majority of patients were female non-smokers of Chinese ethnicity with an incidental adenocarcinoma. EGFR mutations were detected in 62.3% (n=91) of patients, with 93.4% harbouring single-locus mutations, primarily in exons 19 and 21. Co-mutations occurred in 11% (n=10) of EGFRmutant cases, most frequently involving TP53, and less commonly CTNNB1, HER2/ErbB2, and PTEN. Six (6.6%) patients had multi-loci EGFR mutations. Female sex and higher tumour histological grade were independent predictors for EGFR mutations, while former/never smokers showed higher odds on univariate analysis. Among patients tested for PD-L1 (78.8%), 46.6% had negative expression (tumour proportion score <1%), with no correlation to EGFR status.

This is the first genomic molecular profiling study to report exclusively on early-stage NSCLC in Malaysia. The high prevalence of EGFR mutations observed, predominantly involved sensitizing mutations at exons 19 and 21, and was associated with the female sex, a non- smoking status, higher tumour grade, but not PD-L1 expression. Early reflex genomic testing is vital to guide biomarker-driven peri-operative treatment strategies for rNSCLC.