Analysis of mammographic diagnostic errors in breast clinic

Palazzetti, V.; Guidi, Francesca; Ottaviani, Letizia; Valeri, Gianluca; Baldassarre, Silvia; Giuseppetti, Gian Marco

doi:10.1007/s11547-016-0655-0

Cited by 11 publications

(3 citation statements)

References 20 publications

(24 reference statements)

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“…After mass ROI contouring, expertise is still needed for the classification of the identified abnormalities, which is also a challenging task for the radiologists who might be prone to misinterpretation or misdiagnosis due to factors such as inattention, fatigue or lack of experience. For example, in a blind retrospective study in diagnosing mammograms involving 250 known breast cancer patients, a 20.4% false negative rate was reported for 'expert readers' (Palazzetti et al 2016). On the other hand, similar studies showed that 60 ∼ 80% false positives were found in 5 ∼ 10% of the suspected abnormalities recommended for biopsy after follow-up mammograms (Cheng et al 2003, Hussain 2014.…”

Section: Discussionmentioning

confidence: 99%

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis

Wang¹,

Zheng²,

Chen³

et al. 2020

Phys. Med. Biol.

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Objective. To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT). Methods. We retrospectively collected 441 patients who had undergone DBT in which the regions of interest (ROIs) covering the malignant/benign breast mass were extracted for model training and validation. In the proposed MSF framework, three multifaceted representations of the breast mass (gross mass, overview, and mass background) are extracted from the ROIs and independently processed by a multi-scale multi-level features enforced DenseNet (MMFED). The three MMFED sub-models are finally fused at the decision level to generate the final prediction. The advantages of the MMFED over the original DenseNet, as well as different fusion strategies embedded in MSF, were comprehensively compared. Results. The MMFED was observed to be superior to the original DenseNet, and multiple channel fusions in the MSF outperformed the single-channel MMFED and double-channel fusion with the best classification scores of area under the receiver operating characteristic (ROC) curve (87.03%), Accuracy (81.29%), Sensitivity (74.57%), and Specificity (84.53%) via the weighted fusion method embedded in MSF. The decision level fusion-based MSF was significantly better (in terms of the ROC curve) than the feature concatenation-based fusion (p< 0.05), the single MMFED using a fused three-channel image (p< 0.04), and the multiple MMFED end-to-end training (p< 0.004). Conclusions. Integrating multifaceted representations of the breast mass tends to increase benign/malignant mass classification performance and the proposed methodology was verified to be a promising tool to assist in clinical breast cancer screening.

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Section: Discussionmentioning

confidence: 99%

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis

Wang¹,

Zheng²,

Chen³

et al. 2020

Phys. Med. Biol.

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“…The interpretation of imaging models introduces subjectivity due to variations in experience, professional knowledge, and personal bias among physicians. Consequently, different healthcare professionals may yield diverse diagnostic results when analyzing the same set of images ( 12 , 13 ). Furthermore, the utilization of advanced imaging techniques such as PET-PET/CT and whole-body MRI comes with a substantial cost and potential radiation risks, leading to adverse effects on intensive follow-up programs and causing psychological distress ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests

Wei,

Liang,

et al. 2024

Front. Endocrinol.

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BackgroundBreast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management.MethodsWe examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies).ResultsThe STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient’s total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient.ConclusionCombining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

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“…Misses (i.e., false negatives) occur when malignant masses are present but not diagnosed. The miss rate in mammography is estimated at around 30–40% (Lee et al, 2013; Palazzetti et al, 2016). Alarmingly, misses have occurred at a near-constant rate of approximately 30–40% for decades (Harvey et al, 1993; Thomas & McDonald, 1969, as cited in James & Irvine, 1969).…”

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confidence: 99%

Breast tissue density influences tumor malignancy perception and decisions in mammography.

Killingsworth¹,

Bohil²

2021

Journal of Applied Research in Memory and Cognition

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High density breast tissue has been found to reduce radiologists' accuracy in detecting and classifying mammogram abnormalities. The current research examines the perceptual and decisional components that underlie diagnostic classification (independent of detection) in a sample of novices. Mammograms were varied along two dimensions: Breast tissue density (low/high) and the nature of an identified mass (benign/malignant). In two experiments, participants learned to classify images into 4 categories created by factorial combination of these dimensions. In low density tissue, accuracy was higher for benign than for malignant masses. Surprisingly, and in contrast to the mammography literature, accuracy for malignant masses was higher in high-density than in low-density tissue. Cognitive modeling based in general recognition theory (GRT) indicated that low-density/benign category accuracy was largely due to high perceptual discriminability for that category. The high accuracy level for malignant masses in high density tissue was accounted for by decision bound slopes that favored the "malignant" response for items in the high-density malignant category. Because GRT can provide insight into perceptual and decisional determinants of observed classification accuracy, as well as individual-difference level parameters about attention allocation, GRT may be a means to obtain a more detailed understanding of diagnostic classification of complex naturalistic stimuli.

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Analysis of mammographic diagnostic errors in breast clinic

Cited by 11 publications

References 20 publications

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests

Breast tissue density influences tumor malignancy perception and decisions in mammography.

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