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    Breast cancer follow-up
    (Springer International Publishing, 2021) Gurdal, Sibel Ozkan; Canturk, Nuh Zafer
    The targets of breast cancer follow-up are determination of local recurrence and development of secondary tumor, contacting with patients for treatment of local recurrence as quick as possible, evaluation of symptoms and signs related or not related with recurrence or treatment, ensuring of coherence to therapy and psychosocial support, and providing some aid to health-related decision such as pregnancy which may be affected from breast cancer history. All these precision must be taken to consideration according to guidelines and recommendations of multidisciplinary team. © Springer Nature Switzerland AG 2021.
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    Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program
    (Springer, 2024) Seker, Mustafa Ege; Koyluoglu, Yilmaz Onat; Ozaydin, Ayse Nilufer; Gurdal, Sibel Ozkan; Ozcinar, Beyza; Cabioglu, Neslihan; Ozmen, Vahit
    ObjectivesWe aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios.Materials and methodsThe study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping.ResultsThe AI software achieved an AUC of 89.6% (86.1-93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 +/- 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy.ConclusionThis AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%.Clinical relevance statementThe study proposes a more efficient method for screening programs, both in terms of workload and accuracy.Key Points center dot Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers.center dot AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage.center dot AI has the potential to facilitate early diagnosis compared to human reading.Key Points center dot Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers.center dot AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage.center dot AI has the potential to facilitate early diagnosis compared to human reading.Key Points center dot Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers.center dot AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage.center dot AI has the potential to facilitate early diagnosis compared to human reading.
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    Efficacy of Neoadjuvant Chemotherapy in Lobular and Rare Subtypes of Breast Cancer
    (Coll Physicians & Surgeons Pakistan, 2024) Seber, Erdogan Selcuk; Iriagac, Yakup; Cavdar, Eyyup; Karaboyun, Kubilay; Avci, Okan; Yolcu, Ahmet; Gurdal, Sibel Ozkan
    Objective: To determine the predictive factors for the pathological complete response (pCR) in patients with non-ductal invasive breast cancer (ND-BC) receiving neoadjuvant chemotherapy.Study Design: Observational study.Place and Duration of the Study: Departments of Medical Oncology, Tekirdag Namik Kemal University, Sirnak State Hospital, Aydin Adnan Menderes University, Marmara University, Bakirkoy Sadi Konuk Hospital, Basaksehir Cam and Sakura Hospital, Sakarya University, Balikesir Ataturk Hospital, Turkiye, from April 2016 to December 2022.Methodology: A total of 222 non-metastatic breast cancer patients who received neoadjuvant chemotherapy were included in this retrospective multicentric study. The clinicopathologic data were obtained from the hospitals' electronic-record-system. The logistic regression models were used to identify predictive factors for pCR.Results: One hundred and twenty-six patients (56.8%) had invasive lobular carcinoma and 28 patients (12.6%) had signet ring cell/mucinous carcinoma. A total of 45 patients (20.3%) achieved pCR. The pCR rate was 14.3% for lobular carcinoma and 17.9% for signet ring cell/mucinous carcinoma. The univariate analysis showed that estrogen receptor-negative tumours (p = 0.017), high Ki-67 (p = 0.008), high histologic grade (p<0.001), HER2+ expression (p<0.001), and non-lobular histologic type (p = 0.012) were predictive factors for pCR. The multivariate model revealed that HER2 expression (p<0.001) and Ki-67 (p = 0.005) were independent predictors.Conclusion: Neoadjuvant chemotherapy demonstrated effectiveness in ND-BC patients, leading to favourable pCR rates and enabling breast-conserving surgery. Predictive markers for pCR varied depending on histologic types, with HER2 expression, ER status, Ki-67, and histologic grade showing significance in non-ductal subtypes, while HER2 status alone was predictive in lobular carcinoma.
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    Predictive Value of Serum Calprotectin Level in Response to Treatment, a New Inflammatory Marker in Patients with Breast Cancer Requesting Neoadjuvant Treatment
    (Galenos Publ House, 2023) Baydar, Ece; Celikkol, Aliye; Gurdal, Sibel Ozkan; Seber, Selcuk
    Aim: There is a close relationship between inflammation and cancer. Calprotectin is a protein released during inflammation. The aim of this study is to investigate the relationship between breast cancer and calprotectin levels in breast cancer patients receiving neoadjuvant therapy the predictive role of calprotectin in response to treatment. Materials and Methods: In our prospective study, a patient group with 69 breast cancer patients and a control group with 20 patients were formed. Calprotectin was studied from the blood tests taken from the whole sample. Patient data were obtained from the electronic record system. In our study, statistical evaluations were made using a package program called IBM Statistical Package for the Social Sciences Statistics 24. Results: Eighty-nine patients (69 cancer, 20 controls) were included in the study. The median age of breast cancer patients was 48 [minimum (min): 24-maximum (max): 73], the control group was 44.5 (min: 19-max: 68) and the ages of the two groups were similar (p=0.599). Mean calprotectin levels in breast cancer patients were 28.63 +/- 30.5, median 16.5 (min: 6.7-max: 160.7). The mean in the control group was 16.09 +/- 6.1 (min: 8.7-max: 27.4) and there was no statistical difference between the 2 groups (p=0.072). A statistically significant difference was found in terms of calprotectin values according to Ki67 classes (Z=-20.043; p=0.041). Calprotectin values of those with Ki67 class >20 were statistically significantly higher than those with <= 20. Parameters that could predict complete chemotherapy response were evaluated with logistic regression analysis. There was no correlation between calprotectin level and complete response. There was a positive correlation between age increase and complete response. Conclusion: There was no significant difference between serum calprotectin levels of the patient and control groups, but calprotectin level was found to be associated with Ki67 level. There was no relationship between calprotectin and chemotherapy response. Studies with larger sample numbers may make a significant difference.