Impact of breast density on cancer detection: observations from digital mammography test sets

Tapia, Kriscia; Rickard, Mary; McEntee, Mark F.; Garvey, Gail; Lydiard, Lorraine; Brennan, Patrick C.

doi:10.15406/ijrrt.2020.07.00261

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…This is supported by a previous study that found readers show significantly higher sensitivity rates in dense breasts when fewer dense breast cases are included in the dataset. 28 Second, these findings may not completely reflect the performance of this cohort of radiologists in actual screening practice because interval cancers were not considered. However, a previous study 29 comparing the performance of the same cohort of Australian radiologists in both clinical and test settings showed no difference in performance, suggesting that test set data can reasonably predict performance in a clinical setting.…”

Section: Discussionmentioning

confidence: 92%

Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy

Hadadi

Rae

Clark

et al. 2022

Acta Radiologica Open

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Background The impact of radiologists’ characteristics has become a major focus of recent research. However, the markers of diagnostic efficacy and confidence in dense and non-dense breasts are poorly understood. Purpose This study aims to assess the relationship between radiologists’ characteristics and diagnostic performance across dense and non-dense breasts. Materials and methods Radiologists specialising in breast imaging ( n = 128) who had 0.5–40 (13±10.6) years of experience reading mammograms were recruited. Participants independently interpreted a test set containing 60 digital mammograms (40 normal and 20 abnormal) with similarly distributed breast densities. Diagnostic performance measures were analysed via Jamovi software (version 1.6.22). Results In dense breasts, breast-imaging fellowship completion significantly improved specificity ( p = 0.004), location sensitivity ( p = 0.01) and the area under the curve (AUC) of the receiver operating characteristic ( p = 0.03). Only participation in BreastScreen reading significantly improved all performance metrics: specificity ( p = 0.04), sensitivity ( p = 0.005), location sensitivity ( p < 0.001) and AUC ( p < 0.001). Reading > 100 mammograms weekly significantly improved sensitivity ( p = 0.03), location sensitivity ( p = 0.001), and AUC ( p = 0.03).In non-dense breasts, breast fellowship completion significantly improved sensitivity ( p = 0.02), location sensitivity ( p = 0.04) and AUC ( p = 0.002). Participation in BreastScreen reading and reading > 100 mammograms weekly significantly improved only sensitivity ( p = 0.002 and p = 0.003, respectively) and location sensitivity ( p < 0.001 and p < 0.001, respectively). Conclusion Participating in screening programs, breast fellowships and reading > 100 mammograms weekly are important indicators of the diagnostic performance of radiologists across dense and non-dense breasts. In dense breasts, optimal performance resulted from participation in a breast screening program.

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Section: Discussionmentioning

confidence: 92%

Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy

Hadadi

Rae

Clark

et al. 2022

Acta Radiologica Open

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Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases

et al. 2023

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This work aimed to investigate whether global radiomic features (GRFs) from mammograms can predict difficult-to-interpret normal cases (NCs). Assessments from 537 readers interpreting 239 normal mammograms were used to categorise cases as 120 difficult-to-interpret and 119 easy-to-interpret based on cases having the highest and lowest difficulty scores, respectively. Using lattice- and squared-based approaches, 34 handcrafted GRFs per image were extracted and normalised. Three classifiers were constructed: (i) CC and (ii) MLO using the GRFs from corresponding craniocaudal and mediolateral oblique images only, based on the random forest technique for distinguishing difficult- from easy-to-interpret NCs, and (iii) CC + MLO using the median predictive scores from both CC and MLO models. Useful GRFs for the CC and MLO models were recognised using a scree test. The CC and MLO models were trained and validated using the leave-one-out-cross-validation. The models’ performances were assessed by the AUC and compared using the DeLong test. A Kruskal–Wallis test was used to examine if the 34 GRFs differed between difficult- and easy-to-interpret NCs and if difficulty level based on the traditional breast density (BD) categories differed among 115 low-BD and 124 high-BD NCs. The CC + MLO model achieved higher performance (0.71 AUC) than the individual CC and MLO model alone (0.66 each), but statistically non-significant difference was found (all p > 0.05). Six GRFs were identified to be valuable in describing difficult-to-interpret NCs. Twenty features, when compared between difficult- and easy-to-interpret NCs, differed significantly (p < 0.05). No statistically significant difference was observed in difficulty between low- and high-BD NCs (p = 0.709). GRF mammographic analysis can predict difficult-to-interpret NCs.

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A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult

Siviengphanom,

Brennan,

Lewis

et al. 2024

J Digit Imaging. Inform. med.

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This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases for radiology trainees (RTs). Data from 137 RTs were analysed, with each interpreting seven educational self-assessment test sets comprising 60 cases (40 normal and 20 cancer). The study only examined normal cases. Difficulty scores were computed based on the percentage of readers who incorrectly classified each case, leading to their classification as hardest- or easiest-to-interpret based on whether their difficulty scores fell within and above the 75th or within and below the 25th percentile, respectively (resulted in 140 cases in total used). Fifty-nine low-density and 81 high-density cases were identified. Thirty-four GMRFs were extracted for each case. A random forest machine learning model was trained to differentiate between hardest- and easiest-to-interpret normal cases and validated using leave-one-out-cross-validation approach. The model’s performance was evaluated using the area under receiver operating characteristic curve (AUC). Significant features were identified through feature importance analysis. Difference between hardest- and easiest-to-interpret cases among 34 GMRFs and in difficulty level between low- and high-density cases was tested using Kruskal–Wallis. The model achieved AUC = 0.75 with cluster prominence and range emerging as the most useful features. Fifteen GMRFs differed significantly (p < 0.05) between hardest- and easiest-to-interpret cases. Difficulty level among low- vs high-density cases did not differ significantly (p = 0.12). GMRFs can predict hardest-to-interpret normal cases for RTs, underscoring the importance of GMRFs in identifying the most difficult normal cases for RTs and facilitating customised training programmes tailored to trainees’ learning needs.

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Impact of breast density on cancer detection: observations from digital mammography test sets

Cited by 4 publications

References 27 publications

Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy

Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy

Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases

A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult

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