JMIR Cancer最新文献

Background: Previous studies have found and confirmed a correlation between cognitive disorder and chemotherapy. As genetic testing becomes more routine in clinical practice, targeted therapies are increasingly gaining prominence. The relationship between targeted treatment and cognitive function is not yet clear. This study aimed to investigate the correlation between cognitive disorder and targeted treatment by evaluating the changes in cognitive function before and after targeted therapy.

Objective: This study aims to explore whether targeted therapy affects cognitive function in patients with advanced lung cancer and to explore the association between cognitive function, the inflammatory biomarker C-reactive protein, and psychological stress.

Methods: From the screened cohort of 150 patients with advanced non-small cell lung cancer (NSCLC) with gene mutations, 87 (58%) were rigorously selected for the study. The evaluation instruments used were the Mini-Mental State Examination scale, the Distress Thermometer, and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 for assessing quality of life.

Results: A significantly lower progression-free survival (PFS) was observed in the group of patients surviving advanced NSCLC with cognitive disorder under targeted therapy in contrast to survivors in the group with no cognitive disorder (hazard ratio=0.347, 95% CI 0.209-0.578; P<.001). Furthermore, the objective response rate and disease control rate for the group with cognitive disorder were noted to be 37.8% and 86.7%, respectively, contrastingly lower than those in the group with no cognitive disorder, recorded at 78.6% and 97.6%, respectively. Significant variances were also noted in the Mini-Mental State Examination scores between patients with and without cognitive disorder both before and after targeted therapy (P<.001 in both cases), with a decreasing trend observed in both groups after targeted therapy. Noteworthy differences were found in quality of life scores both before and after targeted therapy (P<.001 in both cases). In addition, notable disparities were apparent in C-reactive protein levels among the 2 groups before and after treatment (P=.03 and P=.048 for each time point, respectively), with an upward trend observed in both groups after targeted therapy. The multivariate Cox regression analysis demonstrated that cognitive function is an independent risk factor for PFS in patients with NSCLC receiving targeted therapy.

Conclusions: Cognitive disorder may lead to lower quality of life scores and shorter PFS in patients undergoing targeted therapy. Early screening and intervention for such patients could effectively improve clinical outcomes and quality of life.

Background: Thyroid cancer is one of the most commonly diagnosed malignancies in South Korea, with incidence rates among the highest globally. Young women, in particular, represent a high-risk group, likely due to a combination of biological, occupational, and environmental factors. However, the specific risk factors contributing to thyroid cancer development in this population remain poorly understood.

Objective: This study aims to identify the risk factors associated with thyroid cancer among young female nurses using longitudinal survival analysis.

Methods: This longitudinal study used data from the Korea Nurses' Health Study (KNHS), a prospective national cohort of female nurses. Data from the first, fifth, seventh, and ninth surveys were used to construct a person-period data set. Female nurses aged in their 20s at baseline were included. Time-varying explanatory variables included age, marital status, BMI, smoking, alcohol consumption, perceived stress, sleep problems, nursing position, night shift work, working unit, and duration of radiation exposure. The dependent variable was self-reported physician-diagnosed thyroid cancer. Kaplan-Meier survival analysis and Cox proportional hazards regression were performed to examine the association between risk factors and thyroid cancer occurrence.

Results: A total of 22,759 person-period cases were analyzed, and 105 thyroid cancer events were identified. Kaplan-Meier analysis showed significant associations between thyroid cancer occurrence and age (χ²₁=51.6, P<.001), marital status (χ²₁=25.1, P<.001), sleep problems (χ²₁=20.3, P<.001), night shift work (χ²₁=20.1, P<.001), working unit (χ²₁=13.0, P<.001), and duration of radiation exposure (χ²₁=91.0, P<.001). In the Cox regression model, nurses aged in their 20s had a significantly higher risk of thyroid cancer than those aged in their 30s (hazard ratio [HR] 4.602, 95% CI 1.893-11.188). Those who worked night shifts were also at an increased risk (HR 1.923, 95% CI 1.127-3.280). Compared with no exposure, radiation exposure showed a dose-response relationship: <1 year: HR 3.449, 95% CI 1.474-8.074; ≥1 year: HR 4.178, 95% CI 2.702-6.461.

Conclusions: Younger age, night shift work, and duration of radiation exposure were significantly associated with an increased risk of thyroid cancer in young female nurses. These findings highlight the importance of early screening and occupational risk management, including regular radiation monitoring and support for circadian health, in health care settings.

International registered report identifier (irrid): RR2-10.4178/epih.e2024048.

Background: Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction.

Objective: The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction.

Methods: Retrospective data from 250 patients with CC (2014-2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence-assisted contouring and prognosis prediction system.

Results: Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence-assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction.

Conclusions: The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.

Background: Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users. This study aimed to explore multiprofessional experiences of EHR use in gynecological oncology and to develop a co-designed informatics platform to improve decision-making for ovarian cancer care.

Objective: This study aims to evaluate the perspectives of health care professionals on retrieving routine clinical data from EHRs in the management of ovarian cancer and to design an integrated informatics platform that supports clinical decision-making.

Methods: We conducted a national cross-sectional survey of 92 UK-based professionals working in gynecological oncology, including oncologists, nurses, radiologists, and other specialists in ovarian cancer. The web-based questionnaire, combining quantitative and free-text responses, assessed their experiences with EHR use, focusing on information retrieval, usability challenges, perceived risks, and benefits. In parallel, a human-centered design approach involving health care professionals, data engineers, and informatics experts codeveloped a digital informatics platform that integrates structured and unstructured data from multiple clinical systems into a unified patient summary view for clinical decision-making. Natural language processing was applied to extract genomic and surgical information from free-text records, with data pipelines validated by clinicians against original clinical system sources.

Results: Among 92 respondents, 84 out of 91 (92%) routinely accessed multiple EHR systems, with 26 out of 91 (29%) using 5 or more. Notably, 16 out of 92 respondents (17%) reported spending more than 50% of their clinical time searching for patient information. Key challenges included lack of interoperability (35/141 reported challenges, 24.8%), difficulty locating critical data such as genetic results (57/85 respondents, 67%), and poor organization of information. Only 10 out of 92 professionals (11%) strongly agreed that their systems provided well-organized data for clinical use. While ease of access to patient data was a key benefit, 54 out of 90 respondents (60%) reported lacking access to comprehensive patient summaries. To address these issues, our co-designed informatics platform consolidates disparate patients' data from different EHR systems into a single visual display to support clinical decision-making and audit.

Conclusions: Current EHR systems are suboptimal for support

Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative. However, its acceptance, reliability, and impact on the screening experience remain underexplored.

Objective: This study aimed to explore women's perceptions of AI-enhanced thermography (ThermoBreast) as an alternative to mammography. It aims to identify barriers and motivators related to breast cancer screening and assess how ThermoBreast might improve the screening experience.

Methods: A mixed methods approach was adopted, combining an online survey with follow-up focus groups. The survey captured women's knowledge, attitudes, and experiences related to breast cancer screening and was used to recruit participants for qualitative exploration. After the focus groups, the survey was relaunched to include additional respondents. Quantitative data were analyzed using SPSS (IBM Corp), and qualitative data were analyzed in MAXQDA (VERBI software). Findings from both strands were synthesized to redesign the breast cancer screening journey.

Results: A total of 228 valid survey responses were analyzed. Of 228, 154 women (68%) had previously undergone mammography, while 74 (32%) had not. The most reported motivators were belief in prevention (69/154, 45%), invitations from screening programs (68/154, 44%), and doctor recommendations (45/154, 29%). Among nonscreeners, key barriers included no recommendation from a doctor (39/74, 53%), absence of symptoms (27/74, 36%), and perceived age ineligibility (17/74, 23%). Pain, long appointment waits, and fear of radiation were also mentioned. In total, 18 women (mean age 45.3 years, SD 13.6) participated in 6 focus groups. Participants emphasized the importance of respectful and empathetic interactions with medical staff, clear communication, and emotional comfort-factors they perceived as more influential than the screening technology itself. ThermoBreast was positively received for being contactless, radiation-free, and potentially more comfortable. Participants described it as "less traumatic," "easier," and "a game changer." However, concerns were raised regarding its novelty, lack of clinical validation, and data privacy. Some participants expressed the need for human oversight in AI-supported procedures and requested more information on how AI is used. Based on these insights, an updated screening journey was developed, highlighting improvements in preparation, appointment booking, privacy, and communication of results.

Conclusions: While AI-driven thermography shows promise as

Background: Effective prevention and treatment are urgently needed, since gastric cancer (GC) poses a grave threat to the health and well-being of patients. The 5 East Asian countries (China, Japan, North Korea, South Korea, and Mongolia) represent one of the most significant regions globally in terms of GC burden.

Objective: The goal of this study is to examine the patterns and trends of GC across 5 East Asian countries between 1990 and 2021.

Methods: We retrieved data from the Global Burden of Disease Study (GBD) 2021 regarding the prevalence, incidence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life years (DALYs) associated with GC in 5 East Asian countries from 1990 to 2021. We further assessed the burden of GC according to age and sex. We used decomposition analysis to examine the changes in the number of new cases, patients, and deaths related to GC. We also used Joinpoint (Joinpoint Regression Program, Version 5.1.0) and age-period-cohort analysis methods to interpret the epidemiological characteristics of GC. Autoregressive integrated moving average model (ARIMA) and Bayesian age-period-cohort (BAPC) prediction models were used to forecast the GC burden by 2036.

Results: Among the 5 East Asian countries, China recorded the highest incidence, prevalence, death, YLLs, YLDs, and DALYs in both 1990 and 2021. From 1990 to 2021, the age-standardized rates for prevalence, mortality, incidence, YLDs, YLLs, and DALYs across the 5 East Asian countries showed an overall decline, though they remained higher than the global average. In all 5 East Asian countries, individuals aged 65 years and older consistently exhibited the highest rates for prevalence, incidence, mortality, YLDs, YLLs, and DALYs. The prevalence rate in South Korea, the incidence rate in North Korea and Mongolia, and the mortality rate in China are influenced by aging, surpassing the global aging average.

Conclusions: The disease burden of GC in the 5 East Asian countries has consistently ranked high over the past 3 decades, particularly among the older individuals. The burden of GC in the 5 East Asian countries is expected to present a major public health challenge, primarily driven by the large population size and the aging demographic.