Revolutionizing breast cancer diagnosis: A computer-aided diagnosis framework with Vision Transformers for multistage histopathology-based classification

Gastric cancer is among the most common health conditions on earth and the highest rate of mortality among the causes of cancer. The early and accurate diagnosis plays a big role in increasing patient outcomes and the conventional diagnostic methods, such as endoscopy and biopsy are invasive, time consuming and may give room to the error of human beings. During the past several years, deep learning, and CNNs, in particular, have proven to be an efficient tool of medical image analysis in the framework of computer-aided diagnosis. The most recent trends in CNN-based gastric cancer detectors, segmenters and classifiers are critically summarized and categorized in this review. The article provides a review on 18 representative studies addressing the 4 classification of methodology that are CNN-based classification, segmentation-based approaches, hybrid approach, and ensemble approaches and Generative Adversarial Network (GAN)-based augmentation approaches. The comparative performance analysis shows that there has been a significant advancement in the accuracy of the diagnosis, yet there are still the issues of the low diversity of datasets, the high cost of computation, and the artificially low generalization. Integration of explainable AI, multi-mode imaging, lightweight architecture to be run in real-time, and large-scale clinical validation are the directions of research in the future. This survey is a generalized opinion of the current and the future state of detecting gastric cancer through application of deep learning.

Glaucoma is an eye condition which prompts lasting visual deficiency when the illness advances to a propelled stage. It happens because of high intraocular pressure inside the eye, bringing about harm to the optic nerve. Glaucoma doesn't show any side effects in its initial stage and in this manner, it is critical to analyze ahead of schedule to forestall visual impairment. Fundus photography is broadly utilized by ophthalmologists to aid analysis of glaucoma and is cost-effective. The utilization of Computer Aided Diagnosis(CAD) is successful in the conclusion of glaucoma and can help the clinicians to reduce their remaining task at hand altogether. We have likewise talked about the upsides of utilizing condition of-workmanship procedures, including Deep learning (DL), when building up the robotized framework. The DL techniques are powerful in glaucoma diagnosis. Novel DL calculations with large information accessibility are required to build up a dependable CAD framework. Such procedures can be utilized to analyze other eye diseases precisely. This paper examines the helpfulness of CAD frameworks and deep feed forward neural network to fairly analyze patients with glaucoma. Additionally, unique AI strategies utilized by scientists over the past decade are summarized.

Background: Our team has shown that younger versus older women, and both Blacks and Hispanics breast cancer (BC) survivors versus White women have a higher mortality after a second cancer. We have also shown that county-level neighborhood poverty is associated with increased cancer mortality after a BC diagnosis. In this study, we assess to what extent neighborhood-level socioeconomic status (SES) contributes to high BC mortality after second cancers and compare the association to BC mortality after a first cancer. We hypothesize that social determinants are critical to address in women who develop a second cancer despite now being part of the health system. Methods: Women diagnosed with a first BC and/or second cancer after first BC between 2007 and 2019 were identified from the Maryland (MD) Cancer Registry. Census tract-level neighborhood SES environments for poverty and median income were determined using local spatial autocorrelation analysis with Local Moran’s I of MD’s five-year census-tract estimates from the American Community Survey. MD census tract estimates above or below the state mean were identified as high (H)- or low (L)- tracts, respectively. We defined four neighborhood environments for each SES indicator as H-tracts among H-tracts (HH-tracts); L-tracts among L-tracts (LL-tracts); H-tracts among LL-tracts (HL-tracts); L-tracts among HH-tracts (LH-tracts). Women at least 20 years of age diagnosed with a local or regional primary BC, received surgery, and survived more than 365 days were assigned a tract-level neighborhood SES environment for each SES indicator associated with their first and second BC diagnosis. Time-to-event analyses were used to compare BC deaths across each tract-level SES indicator neighborhood environment separately. The data was analyzed using Multivariate Cox proportional hazard regression models adjusted for age at diagnosis, tract median income, cancer stage, treatment, and Estrogen Receptor status. Models were also stratified by race/ethnicity (Non-Hispanic White, Non-Hispanic Black), age at diagnosis (<50, 50-69, =>70), and treatment (hormone therapy and chemotherapy). Results: The analytical population included 15,149 MD women diagnosed with early-stage BC, of which 2,436 developed a second cancer, and BC deaths after first BC and second cancer diagnosis was 2039 and 315, respectively. We detected a 66% (HR=1.66, 95%CI=1.09, 2.53), 14% (HR=1.14, 95%CI=0.66,1.97), and 15% (HR=1.15, 95%CI=0.73,1.81) increase in BC death for women in LL, LH, and HL median income census tracts when compared to HH median income census tracts in the neighborhood income model after second cancer diagnosis. A 30% (HR=1.30, 95%CI=0.89, 1.90), 31% (HR=1.31, 95%CI=0.90, 1.93), and 20% (HR=1.20 95%CI=0.80, 1.79) increase in BC deaths for women in HH, LH, and HL poverty census tracts when compared to LL poverty census tracts was observed in the neighborhood poverty model after second cancer diagnosis. Notably, we observed increased BC death for women in LL, LH, and HL income neighborhood environments as well as in HH and HL poverty neighborhood environments at first BC diagnosis though not as robust as the second cancer neighborhood environment observations. Finally, neighborhood-level SES environments at first BC diagnosis were not statistically associated with BC death upon second diagnosis. Conclusion: Overall, our findings highlight the importance of neighborhood-level SES, such as poverty and median income, at the time of second cancer diagnosis as a strong indicator of BC mortality after second cancer, possibly even more than after a first BC. These findings provide initial evidence of the need for continued surveillance of neighborhood-level SES factors after a first BC diagnosis, and early interventions to improve survival among this group of high-risk women. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. The impact of neighborhood-level social determinants on breast cancer mortality after a second primary breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-04-03.

The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPI-based CAD framework for breast cancer using privileged information in this work. The work offers both a classifier- or feature-level LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifier-level algorithms and six feature-level LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifier-level and deep learning-based feature-level LUPI algorithms can enhance the performance of a single-modal imaging-based CAD for breast cancer by relocating PI.

Breast cancer (BC) and genital organ cancers (GOC) are known to have a major impact on the quality of life of patients. The aim of this study was to analyze the risk of depression, anxiety, and adjustment disorders in women in Germany with a suspected but unconfirmed diagnosis of BC or GOC in their medical history. This study included women who received a suspected diagnosis of BC or GOC and were followed between 2007 and 2015 (index date). These women were matched (1:1:1) by age to women with a confirmed diagnosis of BC or GOC and women without a cancer diagnosis. The main outcome measure of the study was the rate of depression, anxiety, and adjustment disorder diagnoses within 3years of the index date. The present analysis included a total of 4,842 patients (mean age=49.3years). Within 3years of the index date, 23.5% of women with a confirmed diagnosis of BC or GOC, 14.1% of those with a suspected diagnosis of BC or GOC, and 10.5% of those without a cancer diagnosis developed depression, anxiety, or an adjustment disorder (log-rank p value <0.001). Women with a suspected diagnosis of cancer were at a higher risk for these psychiatric conditions than those without a cancer diagnosis (BC and GOC: HR 1.32; BC: HR 1.21; GOC: HR 1.50). A suspected diagnosis of BC or GOC in a woman's medical history is associated with an increased risk of developing depression, anxiety, and adjustment disorders.

Several studies have demonstrated an increased frequency of cancer in patients with systemic sclerosis (SSc), specially lung and breast cancers. The pathogenesis of the association between SSc and cancer is not fully established. The aim of this study was to describe new cases of the association between SSc and breast cancer and to perform a review of the literature. We retrospectively studied the medical files of eight patients followed in our institution for SSc and breast cancer. We analyzed them with data available in the literature for a total of 46 patients. Cutaneous extension of SSc was clearly mentioned in 17 cases: the SSc was limited in 10 cases and diffuse in 7 cases The median age at the diagnosis of cancer was 54 years (range: 40-71). The median duration between SSc onset and breast cancer diagnosis was 11.5 months (range: 0-288). The duration between SSc onset and breast cancer diagnosis was < or = 12 months in 27 of 44 patients (61.4%), and in 11 (25%) of them the diagnosis of both diseases was made simultaneously. It was clearly mentioned for 35 patients whether the diagnosis of breast cancer was made before or after the onset of SSc. The diagnosis of breast cancer was made before SSc onset in 17 of 35 patients (48.6%) and after SSc onset in 18 of 35 patients (51.4%). For 33 patients, the follow-up was available: 18 (54.5%) died, 11 (33.3%) of them within the 1st year after the diagnosis of the cancer. For none of the patients did the anticancer treatment improve the SSc. The close temporal relationship between SSc onset and breast cancer diagnosis is highly suggestive of a pathophysiological link. SSc is probably not a paraneoplastic disease since the anticancer treatment has no influence on the evolution of SSc. However, it can be suggested that SSc could be a disease facilitating breast cancer and/or metastases development.

The association between cardiovascular disease and breast and gynecologic cancers among black female patients

BACKGROUND: Germline mutations in PALB2, BRCA2 and STK11 are well established as increasing risk of both breast and pancreatic cancer. More recently, ATM and BRCA1 mutations have also been associated with risk, but literature is limited. We investigated the prevalence of pathogenic mutations and likely pathogenic variants (“mutations”) in BRCA1/2, PALB2, STK11 and ATM, comparing mutation occurrence in individuals with diagnoses of breast cancer alone to those with both breast and pancreatic cancer primaries. Prevalence of CDKN2A (p16) mutations was also evaluated in the breast–pancreatic cohort because of its contribution to hereditary pancreatic cancer. METHODS: Clinical histories and test results were reviewed for patients undergoing multi-gene panel testing at one clinical laboratory between April 2012 and June 2015. The study population was limited to women with breast cancer only (n=27,573) and women with both breast and pancreatic cancer (n=97) without other primaries. Patients underwent comprehensive analysis of 5-49 genes, depending on the panel ordered. Demographic and clinical information was provided by clinicians on test requisition forms and pedigrees/clinic notes if provided. Gene-specific mutation frequencies were compared between women with breast cancer only and women with breast and pancreatic cancer using Fisher's exact test. RESULTS: Mutations were identified in BRCA1, BRCA2, PALB2 or ATM in 13 of the 97 breast - pancreatic cancer probands (13.4%) and 1,255 of the 27,573 breast cancer probands (4.6%). Gene-specific mutation frequencies and statistical comparisons may be found in Table 1. ATM mutations were significantly more likely to be identified in women with breast and pancreatic cancer compared to breast cancer alone (Table 1). Interestingly, no CDKN2A or STK11 mutations were identified in the breast plus pancreatic cohort, although this may have been limited by the small number of individuals tested for this gene. Of those 13 women with breast and pancreatic cancers who had identified mutations, 11 (85%) had diagnoses of breast cancer over age 50. Table 1. Mutation FrequenciesGeneMutation Frequence n/N (%)Mutation Frequence n/N (%)pOR (95% PI) Breast &amp; PancreaticBreast Only ATM6/89 (6.74%)209/17,570 (1.19%)0.000766.00(2.12,13.85)BRCA12/90 (2.22 %)429/26,336 (1.63%)0.661.37(0.16,5.14)BRCA24/90 (4.44%)442/26,336 (1.68%)0.0662.72 (0.72,7.28)CDKN2A0/54 (0.00%)13/3,965 (0.33%)10.00 (0, 24.70)PALB21/891.12% 175/17,570 (1%)0.591.13(0.03, 6.54)STK110/860.00% 0/16,931 (0.00)1Inf (0, Inf) CONCLUSION: This exploratory study substantiates the association of deleterious germline ATM mutations with predisposition to both breast and pancreatic cancers. These results also suggest that mutations in ATM may account for a larger portion of inherited breast and pancreatic cancer kindreds than mutations in other well-described genes such as BRCA2, PALB2 and STK11. A personal history of breast and pancreatic cancer may warrant the expansion of current NCCN testing criteria as a single indicator for germline testing, and that pancreatic screening consortia (CAPS) consider inclusion of ATM mutations in screening recommendations. Citation Format: Gordon OK, Childers K, McFarland R, Laduca H. ATM mutations contribution to hereditary breast-pancreatic cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-08-07.

Among patients with breast cancer, tumors that contain estrogen receptors (ER) are associated with improved survival and better response to hormone therapy than those not expressing these receptors. The purpose of these case comparison studies was to examine the relationship between carotenoids, vitamin A, and the tumor ER status in women at diagnosis of primary breast cancer. The focus of the first study was the relationship between dietary intake and ER status, and the focus of the second study was the relationship between ER status and the plasma carotenoid, retinol, and tocopherol concentrations. We evaluated tumor ER status and self-reported dietary intake in 142 women and plasma concentrations of carotenoids, retinol, and tocopherols in 149 women, at diagnosis of breast cancer, before any medical or surgical treatment. In the first study the overall odds of ER-positive status were increased in relation to number of mammograms in the past five years, number of breast-fed babies, dietary carotenoid intake, and more frequent intake of yellow and green vegetables. Overall odds of ER-positive status were decreased in relation to years of oral contraceptive use and preformed vitamin A intake. In the second study older women, women with higher plasma lutein concentration, and women not using beta-carotene supplements were more likely to be ER positive, when data were adjusted for body mass index and factors that may influence breast cancer risk or hormonal status. Significant independent relationships between plasma retinol or tocopherol concentrations and ER status were not observed. The strong and independent relationships between carotenoid intake, plasma lutein concentration, and ER status may relate to observations linking a carotenoid-rich diet with improved prognosis after diagnosis of breast cancer.

BackgroundWomen’s return to work after diagnosis of breast cancer (BC) is becoming more prevalent. However, register-based national investigation on sickness absence (SA) and disability pension (DP) in BC women is lacking. The aim of the study was to explore SA and DP before and after a first BC diagnosis and the possibility to predict new cancer-related SA by using disease-related and sociodemographic factors.MethodsA longitudinal register study of the 3536 women in Sweden aged 19–64 with a first BC diagnosis in 2010 was conducted by linkage of five nationwide registers. Particularly, detailed information on SA and DP was obtained from the National Social Insurance Agency. Descriptive statistics on SA and DP 2 years before through 3 years after the BC diagnosis were performed. The risk of having a new SA spell due to BC or BC-related diagnoses was modeled using logistic regression.ResultsThe proportion of women with SA increased during the year following the BC diagnosis date and declined over the next 2 years to proportions before diagnosis. At the time of BC diagnosis, half of the women began a new SA spell > 14 days with cancer, cancer-related, or mental diagnosis. Disease-related and sociodemographic factors including occupational sector, living area, age, cancer stage, educational level, and number of previous SA days showed statistical significance (p < 0.05) in predicting a new SA around BC diagnosis. By using these factors, it was possible to correctly predict 67% of the new SA spell.ConclusionsSA among women with BC was elevated mainly in the first year after diagnosis. New SA following BC diagnosis can accurately be predicted.

eP061: Genetic risk for breast and ovarian cancer in a diverse and unselected population

Multi-view information fusion in mammograms: A comprehensive overview

This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P &lt; 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P &lt; 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P &lt; 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

Combined anxiety and depressive symptoms before diagnosis of breast cancer

BackgroundMeasures taken to address the COVID-19 pandemic interrupted routine diagnosis and care for breast cancer. The aim of this study was to characterize the effects of the pandemic on breast cancer care in a statewide cohort.Patients and MethodsUsing data from a large health information exchange, we retrospectively analyzed the timing of breast cancer screening, and identified a cohort of newly diagnosed patients with any stage of breast cancer to further access the information available about their surgical treatments. We compared data for four subgroups: pre-lockdown (preLD) 25 March to 16 June 2019; lockdown (LD) 23 March to 3 May 2020; reopening (RO) 4 May to 14 June 2020; and post-lockdown (postLD) 22 March to 13 June 2021.ResultsDuring LD and RO, screening mammograms in the cohort decreased by 96.3% and 36.2%, respectively. The overall breast cancer diagnosis and surgery volumes decreased up to 38.7%, and the median time to surgery was prolonged from 1.5 months to 2.4 for LD and 1.8 months for RO. Interestingly, higher mean DCIS diagnosis (5.0 per week vs. 3.1 per week, p < 0.05) and surgery volume (14.8 vs. 10.5, p < 0.05) were found for postLD compared with preLD, while median time to surgery was shorter (1.2 months vs. 1.5 months, p < 0.0001). However, the postLD average weekly screening and diagnostic mammogram did not fully recover to preLD levels (2055.3 vs. 2326.2, p < 0.05; 574.2 vs. 624.1, p < 0.05).ConclusionsBreast cancer diagnosis and treatment patterns were interrupted during the lockdown and still altered 1 year after. Screening in primary care should be expanded to mitigate possible longer-term effects of these interruptions.Supplementary InformationThe online version contains supplementary material available at 10.1245/s10434-023-13119-w.