Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

Rising cases of drug-induced pulmonary fibrosis: Analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) database, 2000-2022.

PURPOSE: Pulmonary fibrosis (PF) is a severe, progressive disease, which may be caused by exposure to certain medications. METHODS: We queried the U.S. FDA Adverse Event Reporting System (FAERS) from 2000 to 2022, using the search terms "pulmonary fibrosis" and "idiopathic pulmonary fibrosis" and excluded reports with patients under the age of 18 years, and patients with unknown sex or age. Reports were sorted by generic drug names, counted, and plotted over time using a best-fit trendline based on an exponential function. RESULTS: From 2000 to 2022, there were 24 095 935 adverse drug events reported in FAERS, of which 17 520 (0.07%) were reported as PF. After excluding reports containing patients with unknown age (5255, 30%), sex (122, 0.7%), and age below 18 years old (155, 0.9%), our study included 11 988 reports. The mean age of the study sample was 66.5 ± 13.1 years, and 6248 patients (52.1%) were male. Plotting the 11 988 reports by year revealed an exponential best fit line (R2 = 0.88) with a positive slope over time. The top five drug classes associated with PF were disease modifying antirheumatic drugs (DMARDs, 39.4%), antineoplastic agents (26.4%), cardiovascular agents (12.6%), corticosteroids (4.6%), and immunosuppressive agents (4.0%). CONCLUSION: A 23-year analysis of the FAERS database revealed exponentially increasing adverse event reports of PF. Significant annual increases in reporting of PF suspected with DMARDs and antineoplastic agents were identified. Our study highlights important trends, which should be used to guide PF research related to drugs of potential importance.

Appropriate Relevancy and Reliability of Real-World Data for the Utilization of Regulatory Submission.

The extraction of data that contribute to regulatory approval from real-world data (RWD) is difficult because of the lack of a standardized data format and extraction methodology. Additionally, when real-world evidence (RWE) is used as an external control group, the similarity between internal and external control data is not evaluated. To investigate the data extraction methodology for the external control data of rare molecular subtypes, we have initiated the "REALISE" study. In this study, we aim to elucidate the "relevance" and "reliability" of RWD/RWE necessary for regulatory approval. As most databases are not designed for regulatory use in the creation phase, we will investigate retrospective methodologies to ensure RWD/RWE reliability. This study will compare the "relevance" and "reliability" of the ARCAD global database, SCRUM-Japan Registry, SCRUM-Japan observational study, and Flatiron Health RWD, and statistically analyze the differences and similarities among the four databases. We will also examine the methodology for extracting sufficiently relevant data from the SCRUM-Japan observational study. Additionally, if the reliability of the RWD/RWE does not reach the required level for regulatory approval, we will examine the methodologies to ensure the "reliability" of the SCRUM-Japan observational study for regulatory approval. The obtained results will be submitted to the "Consultation for Development of Registry" in the Pharmaceuticals and Medical Devices Agency, and we will discuss the standard methodology. The procedures and findings identified in the REALISE study will be organized from the perspectives of "database construction," "data analysis," and "outcome evaluation" and will be issued as "the draft guidelines."

Patterns in the Reporting of Aggressive Histologic Subtypes in Papillary Thyroid Cancer.

INTRODUCTION: The tall cell, columnar, and diffuse sclerosing subtypes are aggressive histologic subtypes of papillary thyroid cancer (PTC) with increasing incidence, yet there is a wide variation in reporting. We aimed to identify and compare factors associated with the reporting of these aggressive subtypes (aPTC) to classic PTC (cPTC) and secondarily identify differences in outcomes. METHODS: The National Cancer Database was utilized to identify cPTC and aPTC from 2004 to 2017. Patient and facility demographics and clinicopathologic variables were analyzed. Independent predictors of aPTC reporting were identified and a survival analysis was performed. RESULTS: The majority of aPTC (67%) were reported by academic facilities. Compared to academic facilities, all other facility types were 1.4-2.0 times less likely to report aPTC (P < 0.05). Regional variation in reporting was noted, with more cases reported in the Middle Atlantic, despite there being more total facilities in the South Atlantic and East North Central regions. Compared to the Middle Atlantic, all other regions were 1.4-5 times less likely to report aPTC (P < 0.001). Patient characteristics including race and income were not associated with aPTC reporting. Compared to cPTC, aPTC had higher rates of aggressive features and worse 5-y overall survival (90.5% versus 94.5%, log rank P < 0.001). CONCLUSIONS: Aggressive subtypes of PTC are associated with worse outcomes. Academic and other facilities in the Middle Atlantic were more likely to report aPTC. This suggests the need for further evaluation of environmental or geographic factors versus a need for increased awareness and more accurate diagnosis of these subtypes.

Epidemiology of myasthenia gravis in France: Incidence, prevalence, and comorbidities based on national healthcare insurance claims data.

BACKGROUND: The European literature has reported high variability in the incidence and prevalence rates of myasthenia gravis (MG), but no specific epidemiological data for France have been published. This study aimed to assess the incidence and prevalence rates of myasthenia gravis in France based on data extracted from the French National Health Insurance Claims Database (the SNIIRAM database). METHODS: We conducted a retrospective repeated cross-sectional population study from 2008 to 2018 using a representative sample of the French population (Échantillon généraliste des bénéficiaires) covered by health insurance. We calculated the incidence, prevalence, and sex ratio of MG and screened for comorbidities associated with MG (standardized to the general population). RESULTS: In total, 331 MG patients were identified between 2008 and 2018. The average incidence of MG in France was 50 per million person-years, while the mean prevalence was 465 per million people. The female-to-male ratio was 1.33. The Incidence of MG gradually increased from 40years of age for women and 60 for men. Thymoma was present for 5.1% of MG patients and a thymectomy was performed for 4.7%. Thyroid disease was the most prevalent autoimmune comorbidity, affecting approximately 8.5% of cases. MG patients had an increased cancer risk, with a standardized rate ratio of 2.38 (95% CI: 1.64-3.46). CONCLUSION: The incidence and prevalence rates of MG are significantly higher than those previously reported in the literature and the incidence increases with age. The excess risk of cancer raises concerns for MG patients, in particular, concerning the management of immunosuppressive drugs.

Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.

PURPOSE: Immune checkpoint inhibitors (ICIs) have significantly improved the survival of patients with cancer and provided long-term durable benefit. However, ICI-treated patients develop a range of toxicities known as immune-related adverse events (irAEs), which could compromise clinical benefits from these treatments. As the incidence and spectrum of irAEs differs across cancer types and ICI agents, it is imperative to characterize the incidence and spectrum of irAEs in a pan-cancer cohort to aid clinical management. DESIGN: We queried >400 000 trials registered at ClinicalTrials.gov and retrieved a comprehensive pan-cancer database of 71 087 ICI-treated participants from 19 cancer types and 7 ICI agents. We performed data harmonization and cleaning of these trial results into 293 harmonized adverse event categories using Medical Dictionary for Regulatory Activities. RESULTS: We developed irAExplorer (https://irae.tanlab.org), an interactive database that focuses on adverse events in patients administered with ICIs from big data mining. irAExplorer encompasses 71 087 distinct clinical trial participants from 343 clinical trials across 19 cancer types with well-annotated ICI treatment regimens and harmonized adverse event categories. We demonstrated a few of the irAE analyses through irAExplorer and highlighted some associations between treatment- or cancer-specific irAEs. CONCLUSION: The irAExplorer is a user-friendly resource that offers exploration, validation, and discovery of treatment- or cancer-specific irAEs across pan-cancer cohorts. We envision that irAExplorer can serve as a valuable resource to cross-validate users' internal datasets to increase the robustness of their findings.

Prevalence of multiple sclerosis in France in 2021: Data from the French health insurance database.

BACKGROUND: France is among the countries with high prevalence of multiple sclerosis (MS). The most recent estimates are from 2012 and need to be updated because MS prevalence has increased worldwide. OBJECTIVE: To estimate MS prevalence in France on December 31, 2021 and to describe the characteristics of the French MS population using data from the French national health insurance database (SNDS). MATERIAL AND METHODS: Persons with MS (PwMS) were identified in the SNDS database (99% of national coverage) using an algorithm with three criteria: long-term disease status, hospitalizations, and MS-specific drug reimbursements. Crude and sex- and age-stratified prevalence rates were calculated with their 95% confidence intervals as well as the standardized prevalence stratified on the region of residence. RESULTS: In total, 134,062 PwMS were identified (71.8% of women, median age 53.0±14.8years) yielding a prevalence of 197.6 per 100,000 (95% CI [196.5-198.7]). Prevalence rates in women and men were respectively 274.9 (95% CI [273.2-276.6]) and 115.2 (95% CI [114.0-116.4]). In metropolitan France, the highest prevalence rates were observed in the northeastern regions (e.g.>230 PwMS per 100,000 in Grand Est and Hauts-de-France), and the lowest rates in the southwestern regions (∼180 PwMS per 100,000 in Nouvelle-Aquitaine and Occitanie). Overall, 32.1% of PwMS had another long-term disease and 51.8% received at least one MS-specific drug in 2021. CONCLUSION: MS prevalence in France has increased by ∼30% in the last 10years. This increase is probably linked to population ageing, longer survival of PwMS, and the long observation period. The part attributable to a possible increase in MS risk remains to be determined with incidence studies.

Inequities in Time to Treat Thyroid Cancer.

BACKGROUND/AIM: The purpose of this study was to determine socioeconomic and demographic factors which may contribute to inequities in time to treat thyroid cancer. PATIENTS AND METHODS: We used data from the National Cancer Database, 2004-2019, to conduct an analysis of thyroid cancer patients. All (434,083) patients with thyroid cancer, including papillary (395,598), follicular (23,494), medullary (7,638), and anaplastic (7,353) types were included. We compared the wait time from diagnosis to first treatment, surgery, radiotherapy, and chemotherapy for patients based on age, race, sex, location, and socioeconomic status (SES). RESULTS: A total of 434,083 patients with thyroid cancer were included. Hispanic patients had significantly longer wait times to all treatments compared to non-Hispanic patients (first treatment 33.44 vs. 20.45 days, surgery 40.06 vs. 26.49 days, radiotherapy 114.68 vs. 96.42 days, chemotherapy 92.70 vs. 58.71 days). Uninsured patients, patients at academic facilities, and patients in metropolitan areas also had the longest wait times to treatment. CONCLUSION: This study identified multiple disparities related to SES and demographics that correspond to delays in time to treatment. It is crucial that this topic is investigated further to help mitigate these incongruities in thyroid cancer care in the future.

Injury Patterns and Treatment Strategies in Civilian and Military Isolated Abdominal Gunshot Wounds.

BACKGROUND: Combat-related gunshot wounds (GSW) may differ from those found in civilian trauma centers. Missile velocity, resources, logistics, and body armor may affect injury patterns and management strategies. This study compares injury patterns, management, and outcomes in isolated abdominal GSW between military (MIL) and civilian (CIV) populations. METHODS: The Department of Defense Trauma Registry (DoDTR) and TQIP databases were queried for patients with isolated abdominal GSW from 2013 to 2016. MIL patients were propensity score matched 1:3 based on age, sex, and extraabdominal AIS. Injury patterns and in-hospital outcomes were compared. Initial operative management strategies, including selective nonoperative management (SNOM) for isolated solid organ injuries, were also compared. RESULTS: Of the 6435 patients with isolated abdominal GSW, 183 (3%) MIL were identified and matched with 549 CIV patients. The MIL group had more hollow viscus injuries (84% vs. 66%) while the CIV group had more vascular injuries (10% vs. 21%) (p < .05 for both). Operative strategy differed, with more MIL patients undergoing exploratory laparotomy (95% vs. 82%) and colectomy (72% vs. 52%) (p < .05 for both). However, no difference in ostomy creation was appreciated. More SNOM for isolated solid organ injuries was performed in the CIV group (34.1% vs. 12.5%; p < 0.05). In-hospital outcomes, including mortality, were similar between groups. CONCLUSIONS: MIL abdominal GSW lead to higher rates of hollow viscus injuries compared to CIV GSW. MIL GSW are more frequently treated with resection but with similar ostomy creation compared to civilian GSW. SNOM of solid organ injuries is infrequently performed following MIL GSW.

Exploring the complexity and spectrum of racial/ethnic disparities in colon cancer management.

BACKGROUND: Colorectal cancer is a leading cause of morbidity and mortality across U.S. racial/ethnic groups. Existing studies often focus on a particular race/ethnicity or single domain within the care continuum. Granular exploration of disparities among different racial/ethnic groups across the entire colon cancer care continuum is needed. We aimed to characterize differences in colon cancer outcomes by race/ethnicity across each stage of the care continuum. METHODS: We used the 2010-2017 National Cancer Database to examine differences in outcomes by race/ethnicity across six domains: clinical stage at presentation; timing of surgery; access to minimally invasive surgery; post-operative outcomes; utilization of chemotherapy; and cumulative incidence of death. Analysis was via multivariable logistic or median regression, with select demographics, hospital factors, and treatment details as covariates. RESULTS: 326,003 patients (49.6% female, 24.0% non-White, including 12.7% Black, 6.1% Hispanic/Spanish, 1.3% East Asian, 0.9% Southeast Asian, 0.4% South Asian, 0.3% AIAE, and 0.2% NHOPI) met inclusion criteria. Relative to non-Hispanic White patients: Southeast Asian (OR 1.39, p < 0.01), Hispanic/Spanish (OR 1.11 p < 0.01), and Black (OR 1.09, p < 0.01) patients had increased odds of presenting with advanced clinical stage. Southeast Asian (OR 1.37, p < 0.01), East Asian (OR 1.27, p = 0.05), Hispanic/Spanish (OR 1.05 p = 0.02), and Black (OR 1.05, p < 0.01) patients had increased odds of advanced pathologic stage. Black patients had increased odds of experiencing a surgical delay (OR 1.33, p < 0.01); receiving non-robotic surgery (OR 1.12, p < 0.01); having post-surgical complications (OR 1.29, p < 0.01); initiating chemotherapy more than 90 days post-surgery (OR 1.24, p < 0.01); and omitting chemotherapy altogether (OR 1.12, p = 0.05). Black patients had significantly higher cumulative incidence of death at every pathologic stage relative to non-Hispanic White patients when adjusting for non-modifiable patient factors (p < 0.05, all stages), but these differences were no longer statistically significant when also adjusting for modifiable factors such as insurance status and income. CONCLUSIONS: Non-White patients disproportionately experience advanced stage at presentation. Disparities for Black patients are seen across the entire colon cancer care continuum. Targeted interventions may be appropriate for some groups; however, major system-level transformation is needed to address disparities experienced by Black patients.

Validity of diagnoses of respiratory diseases recorded in a Japanese administrative database.

BACKGROUND: Validating the information recorded in administrative databases is essential. However, no study has comprehensively validated the accuracy of Japanese Diagnosis Procedure Combination (DPC) data on various respiratory diseases. Therefore, this study aimed to evaluate the validity of diagnoses of respiratory diseases in the DPC database. METHODS: We conducted chart reviews of 400 patients hospitalized in the departments of respiratory medicine in two acute-care hospitals in Tokyo, between April 1, 2019 and March 31, 2021, and used them as reference standards. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DPC data on 25 respiratory diseases were determined. RESULTS: Sensitivity ranged from 22.2% (aspiration pneumonia) to 100% (chronic eosinophilic pneumonia and malignant pleural mesothelioma) and was <50% for eight diseases, while specificity was >90% for all diseases. PPV ranged from 40.0% (aspiration pneumonia) to 100% (coronavirus disease 2019, bronchiectasis, chronic eosinophilic pneumonia, pulmonary hypertension, squamous cell carcinoma, small cell carcinoma, lung cancer of other histological types, and malignant pleural mesothelioma) and was >80% for 16 diseases. Except for chronic obstructive pulmonary disease (82.9%) and interstitial pneumonia (other than idiopathic pulmonary fibrosis) (85.4%), NPV was >90% for all diseases. These validity indices were similar in both hospitals. CONCLUSIONS: The validity of diagnoses of respiratory diseases in the DPC database was high in general, thereby providing an important basis for future studies.

Radiotherapy Deserts: The Impact of Race, Poverty, and the Rural-Urban Continuum on Density of Providers and the Use of Radiation Therapy in the US.

PURPOSE: Prior efforts to characterize disparities in radiation therapy access and receipt have not comprehensively investigated interplay between race, socioeconomic status, and geography relative to oncologic outcomes. This study sought to define these complex relationships at the US county level for prostate cancer (PC) and invasive breast (BC) cancer to build a tool that facilitates identification of "radiotherapy deserts"-regions with mismatch between radiation therapy resources and oncologic need. METHODS AND MATERIALS: An ecologic study model was constructed using national databases to evaluate 3,141 US counties. Radiation therapy resources and use densities were operationalized as physicians to persons at risk (PPR) and use to persons at risk (UPR): the number of attending radiation oncologists and Medicare beneficiaries per 100,000 persons at risk, respectively. Oncologic need was defined by "hot zone" counties with ≥2 standard deviations (SDs) above mean incidence and death rates. Univariable and multivariable logistic regressions examined links between PPR and UPR densities, epidemiologic variables, and hot zones for oncologic outcomes. Statistics are reported at a significance level of P < .05. RESULTS: The mean (SD) PPR and UPR densities were 2.1 (5.9) and 192.6 (557.6) for PC and 1.9 (5.3) and 174.4 (501.0) for BC, respectively. Counties with high PPR and UPR densities were predominately metropolitan (odds ratio [OR], 2.9-4.4), generally with a higher percentage of Black non-Hispanic constituents (OR, 1.5-2.3). Incidence and death rate hot zones were largely nonmetropolitan (OR, 0.3-0.6), generally with a higher percentage of Black non-Hispanic constituents (OR, 3.2-6.3). Lower PPR density was associated with death rate hot zones for both types of cancer (OR, 0.8-0.9); UPR density was generally not linked to oncologic outcomes on multivariable analysis. CONCLUSIONS: The study found that mismatch between oncologic need with PPR and UPR disproportionately affects nonmetropolitan communities with a higher percentage of Black non-Hispanic constituents. An interactive web platform (bit.ly/densitymaps) was developed to visualize "radiotherapy deserts" and drive targeted investigation of underlying barriers to care in areas of highest need, with the goal of reducing health inequities in this context.

Characteristics Contributing to Survival Differences Between Black and White Patients Following Cystectomy.

PURPOSE: Examine patient, tumor, and treatment characteristics effect on the disparity between black and white patients with muscle-invasive bladder cancer (MIBC) who undergo radical cystectomy (RC). METHODS: 1,286 black patients in the 2004 to 2016 National Cancer Database fit inclusion criteria. A tapered match was performed from 17,374 white patients sequentially matched to the black cohort on demographics (age, gender, insurance, income, education, county, diagnosis year), presentation (demographic variables, stage, grade, tumor size, Charlson score), and treatment (demographic and presentation variables, lymph node count, hospital volume, neoadjuvant chemotherapy [NAC], treatment delay), creating 3 matched cohorts. Chi-square and Kruskal-Wallis tests were used to compare cohorts. Kaplan-Meier analysis was used to compare 5-year overall survival (OS). RESULTS: 5-year OS rate was 40.4% and 35.6% for unmatched white and black cohorts (P < 0.001), respectively. Following demographics and presentation match, 5-year OS rate for white patients decreased to 39.2% (P = 0.003) and 39.10% (P = 0.019), respectively. After treatment match, 5-year OS rate decreased to 36.7% for white patient (P = 0.32). Following presentation match, 7.2% of black patients vs. 5.8% of white patients had treatment delay, and 10.1% of black patients vs. 11.2% of white patients received NAC. The treatment match resulted in a 0.3% difference between groups for treatment delay and NAC. CONCLUSIONS: Our analysis demonstrates that disparity between black and white patients with muscle-invasive bladder cancer exists in demographic-, presentation-, and treatment-related variables. Treatment variables may be a large contributing factor to survival disparities. Further research is needed to identify social, biological, and organizational inputs that contribute to these disparities.

Breast Cancer Stage at Diagnosis in a Nigerian Hospital: Trend over a Decade.

BACKGROUND: Breast cancer outcomes are poor in Nigeria, partly due to late-stage diagnosis. To assess the impact of past and present interventions, a review of an institutional data base was performed to determine the trend with respect to stage at diagnosis over a decade. METHODS: A retrospective analysis of an institutional data base was performed over a decade. The review period was divided into segments (2013-2015, 2016-2018 and 2019-2022). The latter period (2019-2022), corresponds to a time of more intense community interventions aimed at promoting early diagnosis and treatment around the locality of the teaching hospital. The stage at diagnosis within these periods were compared. The relationship between stage and sociodemographic variables (age, religion, marital status, menopausal status, and level of education) was assessed. RESULTS: There were 910 patients, 144 within the first period (2013-2015), 318 in the second (2016-2018) and 454 in the third period (2019-2022). The majority presented with stage 3 disease (73.8%). There was a significant decline in the proportion of metastatic cases and a concomitant increase in early cases in the latter part of the review. The proportion of metastatic cases reduced from 23.6% (2013-2015), to 15.7% (2016-2018) and 9.3% (2019-2022) (p=0.001). Level of education was significantly associated with stage (p=0.001). CONCLUSION: This study shows a trend towards downstaging of breast cancer in our locality. This trend corresponds to the period of more intense community interventions, justifying the need to further interrogate the direct impact of each of the interventions. This will provide a firm basis for sustaining or improving on current efforts to achieve more robust gains.

CONTEXTE: Les résultats du cancer du sein sont médiocres au Nigeria, en partie en raison d'un diagnostic tardif. Pour évaluer l'impact des interventions passées et actuelles, une revue d'une base de données institutionnelle a été réalisée pour déterminer la tendance en ce qui concerne le stade au moment du diagnostic sur une décennie. MÉTHODES: Une analyse rétrospective d'une base de données institutionnelle a été effectuée sur une décennie. La période de revue a été divisée en segments (2013-2015, 2016-2018 et 2019-2022). La dernière période (2019-2022) correspond à une période d'interventions communautaires plus intensives visant à promouvoir un diagnostic précoce et un traitement autour de la localité de l'hôpital universitaire. Les stades au moment du diagnostic au cours de ces périodes ont été comparés. La relation entre le stade et les variables sociodémographiques (âge, religion, état matrimonial, statut ménopausique et niveau d'éducation) a été évaluée. RÉSULTATS: Il y avait 910 patients, 144 dans la première période (2013-2015), 318 dans la deuxième (2016-2018) et 454 dans la troisième période (2019-2022). La majorité présentait une maladie de stade 3 (73,8 %). On a observé une diminution significative de la proportion de cas métastatiques et une augmentation concomitante des cas précoces dans la dernière partie de la revue. La proportion de cas métastatiques est passée de 23,6 % (2013-2015) à 15,7 % (2016- 2018) et 9,3 % (2019-2022) (p=0,001). Le niveau d'éducation était significativement associé au stade (p=0,001). CONCLUSION: Cette étude montre une tendance à la réduction du stade du cancer du sein dans notre localité. Cette tendance correspond à la période d'interventions communautaires plus intenses, justifiant la nécessité d'interroger davantage l'impact direct de chacune des interventions. Cela fournira une base solide pour soutenir ou améliorer les efforts actuels afin d'obtenir des gains plus robustes. Mots-clés: Sein, Cancer, Stade, Nigéria.

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.

Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation.

Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model.