Delayed time from first medical visit to diagnosis for breast cancer patients in Taiwan

The Role of the Health Belief Model in Explaining Why Symptomatic Iranian Women Hesitate to Seek Early Screening for Breast Cancer: a Qualitative Study

Early detection of breast cancer (BC) is important to increase the likelihood of treatment and improve the quality of life and survival. The study explored why symptomatic women linger to seek early screening for BC diagnosis based on the health belief model (HBM). In this qualitative study, 20 participants were selected using the purposive sampling method (9 health professionals and 11 female patients BC). Data were collected using in-depth, semi-structured interviews in 2019. Transcribed interview data were analyzed using the directed content analysis informed by HBM. According to the participants' experiences, although most patients comprehended the extent of the disease, they did not see themselves at risk of BC. Some were not adequately aware of the benefits of early diagnosis and did not have the self-efficacy required for early presentation. The main barriers to the early presentation included lack of awareness, financial problems, embarrassment over the clinical examination, and inadequate access to specialized centers. According to HBM, while designing and implementing educational programs, it is recommended to focus on reinforcing perceived susceptibility, perceived benefits, and perceived self-efficacy, providing access to facilities, and removing possible barriers and cultural issues to encourage women for timely referral for BC screening.

Breast Cancer Care Timeliness Framework: A Quality Framework for Cancer Control

PURPOSE

The aim of this study is to determine the pathway that women follow for Breast Cancer Care (BCC) and the time intervals from symptom discovery to treatment initiation and to develop a quality matrix framework.

METHODS

A retrospective cohort study was conducted at six tertiary centers in Malaysia. All women with newly diagnosed breast cancer were interviewed, and a medical records review was conducted using a structured questionnaire. The BCC timeliness framework showed that the total time between a woman discovering their first breast changes and the date of initial treatment was divided into three distinct intervals: presentation interval, diagnostic interval, and treatment interval. Four diagnosis subintervals, referral, biopsy, report, and diagnosis resolution intervals, were also looked into.

RESULTS

The BCC timeliness framework was used to capture important time points. The median total time, presentation interval, diagnostic interval, and treatment interval were 4.9 months (range, 1 month to 10 years), 2.4 months (range, 7 days to 10 years), 26 days (range, 4 days to 9.3 months), and 21 days (range, 1 day to 7.2 months), respectively. Meanwhile, the median time for the diagnosis subinterval of referral, biopsy, report, and diagnosis resolution was 8 days (range, 0 day to 8 months), 0 day (range, 0 day to 20 days), 7 days (range, 3 days to 3.5 months), and 4 days (range, 1 day to 1.8 months), respectively.

CONCLUSION

The BCC timeliness framework is based on the current sequenced trajectory of the BCC journey. Clarity in the measurement of timeliness provides a standardized language for monitoring and outcome research. It can serve as a quality indicator for community and hospital-based breast cancer programs.

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

BACKGROUND

The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.

QUESTIONS/PURPOSES

(1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions?

METHODS

All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study.

RESULTS

The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively.

CONCLUSION

SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Late-Stage Diagnosis and Associated Factors Among Breast Cancer Patients in South and Southwest Ethiopia: A Multicenter Study

BACKGROUND

Breast cancer is a leading cause of cancer death in women in low- and middle-income countries, largely because of late-stage diagnosis. Yet studies are very limited in the Ethiopian context. Therefore, we determined the occurrence of late-stage disease and associated factors in selected public hospitals in south and southwest Ethiopia.

PATIENTS AND METHODS

A 5-year retrospective cross-sectional study was conducted on breast cancer patient medical records from January 2013 to December 2017 in 6 hospitals. Multivariable logistic regression was performed to identify factors associated with late-stage disease (stage III and IV). Adjusted odds ratios (AOR) with 95% confidence intervals were used. P < .05 was considered statistically significant.

RESULTS

Overall, 426 breast cancer patients were identified, and 72.5% were diagnosed with late-stage disease. The mean ± standard deviation patient age was 42.8 ± 13.4 years. Factors associated with late diagnosis were patient delay in seeking care (AOR = 2.50; 95% confidence interval [CI], 1.51-4.16); health system delays (AOR = 1.62; 95% CI, 1.02-2.59); female sex (AOR = 3.46; 95% CI, 1.50-7.98); rural residence (AOR = 2.37; 95% CI, 1.45-3.86); chief complaint of breast lump (AOR = 3.01; 95% CI, 1.49-6.07); and history of comorbidities (AOR = 1.72; 95% CI, 1.02-2.91).

CONCLUSION

The majority of patients were diagnosed with late-stage diagnosis of breast cancer. Patient delays in seeking care, health system delays, being female, rural residence, and patient comorbidities were associated factors. These findings provide evidence that efforts to increase public and health provider awareness to promote early breast cancer diagnosis, particularly in rural areas, are needed in south and southwest Ethiopia.

Factors Associated with Time Intervals for Diagnosis of Colorectal Cancer: A Hospital Based Study in Khon Kaen, Thailand

Background:

Colorectal cancer (CRC) is among the five-leading cancers in Thailand. Delayed diagnosis is crucial for undermining the prognosis of the patients. This study aims to evaluate the factors associated with the time interval for diagnosis (TID).

Methods:

A cross-sectional analytical study of 191 CRC patients with histological confirmation who were undergoing treatment in the tertiary hospital in Khon Kaen Province was conducted. The data were obtained by interview and retrieving from medical records. The time interval in each diagnostic process is reported in geometric mean. The geometric mean ratio (GMR) used to interpret the results from multiple linear regressions that analyze the relationship between factors and log-transformed TID.

Results:

Most patients were males (61.78%) with mean age of 61.28±10.2 years old. The geometric mean of TID was 263.48 days. Two factors were significantly associated with longer TID: first visit at a tertiary hospital (GMR=7.77 relative to secondary hospital; 95%CI=1.95 to 30.57) and distance to tertiary healthcare. Two factors were significantly associated with shorter TID: officer/ state enterprise (GMR=0.53 relative to agriculture; 95%CI=0.28 to 0.98) and cost of traveling to secondary healthcare.

Conclusions:

The results showed the occupation, first health care visit, distance and cost were factors associated with TID. Improving the facilities at the secondary healthcare units for diagnosing CRC would be likely to help to reduce the wasted time in the healthcare system.

Time Interval between Self-Detection of Symptoms to Treatment of Breast Cancer

Background:

In India breast cancer is the number one cancer among females with an incidence rate of 25.8 per 100,000 women and mortality of 12.7 per 100,000 women. India continues to have a low 5-year survival rate of breast cancer with only 66.1% as compared to 90% in developed countries. The major reason for low survival is that patients are diagnosed with cancer at high stage. The present study attempts to delineate the time interval between self-detection of breast cancer symptoms and seeking care and to find the main reasons for delay in seeking care.

Methods:

A cross sectional study was undertaken from October 2016 to March 2017 in a population based cancer registry (PBCR) and hospital based cancer registry (HBCR) located in south of India. Histologically confirmed breast cancer patients (N=181) were interviewed at hospital using a pre-tested semi structured questionnaire.

Results:

The median time interval between the self-detection of breast cancer symptoms and first contact with general physician was 60 [IQR 30-180] days. The median time to diagnosis from the first contact was 30 [IQR 10 - 60] days and the overall median time span from self-detection of symptoms to treatment was 150 [IQR 95-265] days. The major reason given for not seeking medical care in time was lack of awareness in identifying the cancer symptoms both among patients and primary care providers.

Conclusion:

There was considerable delay from self-detection of symptoms to cancer specific primary treatment of breast cancer. We found lack of awareness among patients as well as in primary care providers to be the major concern for delay. Awareness among the target population and health care professionals would have to be improved for early diagnostics and access to care.

Detection and classification the breast tumors using mask R-CNN on sonograms

Breast cancer is one of the most harmful diseases for women with the highest morbidity. An efficient way to decrease its mortality is to diagnose cancer earlier by screening. Clinically, the best approach of screening for Asian women is ultrasound images combined with biopsies. However, biopsy is invasive and it gets incomprehensive information of the lesion. The aim of this study is to build a model for automatic detection, segmentation, and classification of breast lesions with ultrasound images. Based on deep learning, a technique using Mask regions with convolutional neural network was developed for lesion detection and differentiation between benign and malignant. The mean average precision was 0.75 for the detection and segmentation. The overall accuracy of benign/malignant classification was 85%. The proposed method provides a comprehensive and noninvasive way to detect and classify breast lesions.

Pathways to care for patients in Pakistan experiencing signs or symptoms of breast cancer

Multiple social and financial barriers exist to breast cancer detection in Pakistan, which may cause a delay in seeking care and the final diagnosis. This analytical study documents the pathways and time courses associated with referral to diagnostic centres to evaluate the clinical signs and symptoms of breast cancer. This report also verifies the association between socio-demographic and clinical indicators concerning the length of time spent before reaching diagnostic facilities. A purposive sample of 200 patients was selected from two tertiary care hospitals in Lahore, Pakistan, for the interviews. Descriptive statistics (that is, percentages, frequencies, and measures of central tendencies) and a multiple linear regression model were used to achieve the study objectives. The descriptive model showed 31-128 days interval between a patient's awareness of a clinical sign or symptom and receiving care. The healthcare system, including traditional healers, took from 7 to 194 days, and the time to diagnosis ranged from 15 to 30 days. Pain severity, larger tumour size, lack of clinical improvement, and the desire to obtain better treatment were reasons given for seeking care, but lack of awareness and fear of financial burden related to accessing healthcare facilities were identified as barriers. Moreover, socio-demographic and other predictive clinical factors were potentially associated with and substantially influenced the likelihood of the increased length of breast cancer patients' time to reach diagnostic centres. In conclusion, referrals by multiple healthcare providers, especially traditional healers and general practitioners, was a significant predictor for delay in diagnosis. Therefore, increased awareness and a responsive healthcare system may reduce the time from the recognition of symptoms to the early detection of breast cancer among women, thus improving outcomes in a developing country.

Delay in breast cancer diagnosis: a Brazilian cohort study

OBJECTIVES

To evaluate the delay in breast cancer (BC) diagnosis and its risk factors.

STUDY DESIGN

A cohort study of BC patients referred to treatment at oncological reference hospital, Brazil. Delay in BC diagnosis was defined as a time interval ≥90 days between the first contact with a care provider and a BC diagnosis.

METHODS

The association between independent variables and delay was performed by univariate analysis and multiple logistic regression.

RESULTS

Five hundred and twenty-six women were included in the study. Delay was observed in 68.8% and was associated with performing histopathological examination at oncological reference hospital (odds ratio [OR]: 3.96, 95% confidence interval [CI]: 1.91-8.20) or at another public health service (OR: 2.31; 95% CI: 1.50-3.56) and attending gynecological consultations annually (OR: 3.24; 95% CI: 1.97-5.33) or every 2-3 years (OR: 2.86; 95% CI: 1.55-5.28). Patients who presented a lump as the first sign or symptom had a lower chance of delay (OR: 0.43; 95% CI: 0.29-0.65).

CONCLUSIONS

Improvements in the structure and access to health services are needed to reduce the time to diagnosis.

Socioeconomic Factors, Health Behavior, and Late-Stage Diagnosis of Breast Cancer: Considering the Impact of Delay in Diagnosis

BACKGROUND

Stage of cancer at diagnosis is one of the most important factors in patient prognosis. By controlling for diagnostic delay, this study aimed to identify factors associated with late-stage breast cancer (BC).

PATIENTS AND METHODS

From November 2014 to January 2017, required information on 497 patients who were newly diagnosed with BC was obtained from patients' medical records. Logistic regression was used to measure the association between cancer stage and study variables.

RESULTS

Only 18.3% of patients were diagnosed at stage I. The rest were diagnosed at stage II (45.5%) or higher (36.2%). Among those with ≤ 3 months' diagnostic delay, age (odds ratio [OR] = 0.96; 95% confidence interval [CI], 0.93-0.99), place of residence (OR urban/rural = 1.72; 95% CI, 1.42-1.93), income (OR high/low = 0.27; 95% CI, 0.10-0.72), performing breast self-examination (OR yes/no = 0.51; 95% CI, 0.0.26 -0.98), smoking (OR yes/no = 2.23; 95% CI, 1.37-3.62), history of chest X-ray (OR yes/no = 1.40; 95% CI, 1.16-1.98), presence of chronic diseases (OR yes/no = 1.73; 95% CI, 1.36-5.48), and, for those with a delay of > 3 months, marriage age (OR = 0.83; 95% CI, 0.73-0.94), income (OR high/low = 0.07; 95% CI, 0.008-0.63), family history of BC (OR = 3.82; 95% CI, 1.05-5.05), daily exercise (OR < 10/10-20 = 0.10; 95% CI, 0.01-0.67), and presence of chronic diseases (OR yes/no = 1.77; 95% CI, 1.73-5.07), were associated with late-stage of cancer.

CONCLUSION

Shortening the diagnostic delay can help patients receive medical treatment at an earlier disease stage, resulting in better prognosis. Smokers, younger women, and those with chronic conditions or a family history of BC should take extra caution, as they may have worse prognosis if diagnosed with cancer.

Assessment of duration until initial treatment and its determining factors among newly diagnosed oral cancer patients: A population-based retrospective cohort study

Few studies have focused on the early treatment stages of cancer, and the impact of treatment delay on oncologic outcomes is poorly defined. We used oral cancer as an example to investigate the distribution of durations until initial treatment.This study was conducted using the National Health Insurance Research Database, which is linked to Taiwan's Cancer Registry and Death Registry databases. We defined "cutoff points for first-time treatment" according to a weekly schedule and sorted the patients into 2 groups based on whether their duration until initial treatment was longer or shorter than each cutoff. We then calculated the Kaplan-Meier estimator to determine the difference in survival rates between the 2 groups and performed logistic regression to identify determining factors.The average time between diagnosis and initial treatment was approximately 22.45 days. The average survival duration was 1363 days (standard deviation: 473.06 days). Oral cancer patients had no statistically significant differences in survival until a cutoff point of 3 weeks was used (with survival duration 71 days longer if initial treatment was received within 3 weeks). Patients with higher incomes or higher Charlson comorbidity index scores and patients treated at a hospital in a region with medium urbanization had lower likelihoods of treatment delay, whereas older patients were at higher risk of treatment delay.The attitudes, beliefs, and social contexts of oral cancer patients influence the treatment-seeking behaviors of these patients. Therefore, the government should advocate the merits of the referral system for cancer treatment or improve quality assurance for cancer diagnoses across different types of hospitals. Health authorities should also educate patients or use a case manager to encourage prompt treatment within 3 weeks and should provide screening and prevention services, particularly for high-risk groups, to reduce mortality risk.

The Treatment Approach and Social Support Needs for Patients with Breast Cancer

OBJECTIVE

Breast cancer is the most common type of cancer in women both in developed and developing countries. It has a higher mortality rate in low and middle income countries due to the late-stage diagnosis. The principal aim of this study was to investigate what patients with breast cancer did before presenting to Turgut Özal Medical Center and its relationship with late stage diagnosis. The study also aimed to identify the level of patients' perceived social support.

MATERIALS AND METHODS

The study included 200 patients with breast cancer who were treated at the chemotherapy unit during 2013 and 2014.

RESULTS

The mean age of the patients was 51.16±1.10 years and 60% of the women were graduates of elemantary school. The majority of patients (69.5%) noticed breast mass as the first symptom and 56.5% were diagnosed at later stages. Thirty-four percent of the patients delayed their visit to a health care centers after realizing the first symptom. No statistically significant relationship was determined between women's education level, residential area, age, the first symptom noticed, stages of tumor, and patients and system-related delay (p>0.05). In terms of family history of breast cancer, there was a significant difference between patient-related and system-related delays (p<0.05). The family support score (24.8±4.6) was higher than those of friends and husbands (23.8±5.5, 21.3±6.4, respectively). The husband support score was statistically different in terms of intimacy between women and their husbands after disease (p<0.001).

CONCLUSION

It can be concluded that overcoming barriers related to patients and the system will lead to early-stage diagnosis, which in turn will result in higher survival rates of patients with breast cancer. As awareness and knowledge level of women about cancer increases, they will visit health care centers earlier where they can receive more comprehensive treatment.

Identifying predictors of delayed diagnoses in symptomatic breast cancer: a scoping review

Delayed diagnosis of breast cancer can adversely impact patient outcomes. To better understand this issue, we conducted a scoping review of literature from 1996 to 2015 that examined predictors of delayed presentation and diagnosis among women with breast cancer symptoms. We performed searches to identify studies of predictors of the time from symptom onset to first presentation (the patient interval), first presentation to diagnosis (the diagnostic interval) and symptom onset to diagnosis (the total interval) among women with breast cancer symptoms. Two reviewers independently reviewed and abstracted the findings of these studies. Of the 22 studies included in this review, 16 examined predictors of the patient interval, six examined predictors of the diagnostic interval and four examined predictors of the total interval. A total of 116 predictors were examined; three had evidence of an association with a less timely diagnosis: not disclosing symptoms to others, non-lump breast symptoms and African American ethnicity. Improving awareness of atypical breast cancer symptoms and encouraging disclosure could improve the timeliness of a breast cancer diagnosis. Access concerns for vulnerable groups need to be addressed. Future research should be more purposeful in its choice of predictors, possibly through the use of established conceptual frameworks.