KEYWORDS: Mammography, Diagnostics, Breast density, Education and training, Breast cancer, Breast, Cancer, Cancer detection, Tissues, Statistical analysis
Previous research has revealed that Vietnamese radiologists had lower diagnostic efficacy in interpreting mammograms than radiologists from Western countries. This study investigated the improvement in diagnostic performances of Vietnamese doctors in breast cancer detection via VIETRAD (VIEtnam: Transformation of Radiological Detection) program. Data of 33 participants who completed three training sessions containing normal and cancer mammographic cases from Australia and Vietnam were assessed in sensitivity, specificity, ROC and JAFROC. Results show that Vietnamese doctors have improved their diagnostic accuracy in identifying normal and cancer cases on mammograms across different levels of breast density.
The global radiomic signature extracted from mammograms can indicate that malignancy appearances are present within an image. This study focuses on a set of 129 screen-detected breast malignancies, which were also visible on the prior screening examinations (i.e., missed cancers based on the priors). All cancer signs on the prior examinations were actionable based on the opinion of a panel of three experienced radiologists, who retrospectively interpreted the prior examinations (knowing that a later screening round had revealed a cancer). We investigated if the global radiomic signature could differentiate between screening rounds: when the cancer was detected (“identified cancers”), from the round immediately before (“missed cancers”). Both identified cancers and “missed cancers” were collected using a single vendor technology. A set of “normals”, matched based on mammography units, was also retrieved from a screening archive. We extracted a global radiomic signature, containing first and second-order statistics features. Three classification tasks were considered: (1) “identified cancers” vs “missed cancers”, (2) “identified cancers” vs “normals”, (3) “missed cancers” vs “normal”. To train and validate the models, leave-one-case-out cross-validation was used. The classifier resulted in an AUC of 0.66 (95%CI=0.60-0.73, P<0.05) for “missed cancers” vs “identified cancers” and an AUC of 0.65 (95%CI=0.60-0.69, P<0.05) for “normals” vs “identified cancers”. However, the AUC of the classifier for differentiating “normals” from “missed cancers” was at chance-level (AUC=0.53 (95%CI=0.48-0.58, P=0.23). Therefore, eliminating some of these “missed” cancers in clinical practice would be very challenging as the global signal of the malignancy that help with a diagnosis, are at best weak.
This study aimed at conducting a review of the prior mammograms of screen-detected breast cancers, found on full-field digital mammograms based on independent double reading with arbitration. The prior mammograms of 607 women diagnosed with breast cancer during routine breast cancer screening were categorized into “Missed”, “Prior Vis”, and “Prior Invis” . The prior mammograms of “Missed” and “Prior Vis” cases showed actionable and non-actionable visible cancer signs, respectively. The “Prior Invis” cases had no overt cancer signs on the prior mammograms. The percentage of cases classified as “Missed”, “Prior Vis”, and “Prior Invis” categories were 25.5%, 21.7%, 52.7%, respectively. The proportion of high-density cases showed no significant differences among the three categories (p-values<0.05). The breakdown of cases into “Missed”, “Prior Vis”, and “Prior Invis” categories did not differ between invasive (488) and in-situ (119) cases. In the invasive category, the progesterone (p-value=0.015) and estrogen (p-value=0.007) positivity and the median ki-67 score (p-value=0.006) differed significantly among the categories with the “Prior Invis” cases exhibiting the highest percentage of hormone receptors negativity. In the invasive cases, the percentage of cancers graded as 3 (i.e., more aggressive) were significantly more in the “Prior Invis” category compared to both “Missed” and “Prior Vis” categories (both p-values<0.05). The status of receptors and breast cancer grade for the in-situ cases did not differ significantly among the three categories. Prior images categorization can predict the aggressiveness of breast cancer. Techniques to better interrogate prior images as shown elsewhere may yield important patient outcomes.
The initial impressions about the presence of abnormality (or gist signal) from some radiologists are as accurate as decisions made following normal presentation conditions while the performance from others is only slightly better than chance-level. This study investigates if there is a subset of radiologists (i.e., “super-gisters”), whose gist signal is more reliable and consistently more accurate than others. To measure the gist signal, images were presented for less than a half-second. We collected the gist signals from thirty-nine radiologists, who assessed 160 mammograms twice with a wash-out period of one month. Readers were categorized as “super-gisters” and “others” by fitting a mixture of Gaussian models to the average Area Under Receiver Operating Characteristics curve (AUC) values of radiologists in two rounds. The median intra-class correlation (ICC) for the “supergisters” was 0.63 (IQR: 0.51-0.691) while the median ICC for the “others” was 0.51 (IQR: 0.42-0.59). The difference between the two groups was significant (p=0.015). The number of mammograms interpreted by the radiologist per week did not differ significantly between “super-gisters” and others (medians of 237 versus 200, p=0.336). The linear mixed model, which treated both case and reader as random variables showed that only “super-gisters” can perceive the gist of the abnormal on negative prior mammograms, from women who developed breast cancer. Although detecting gist signal is noisy, a sub-set of readers have the superior capability in detecting the gist of the abnormal and only the scores given by them are useful and reliable for predicting future breast cancer.
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