Selective phenotyping, entropy reduction, and the mastermind game.

BACKGROUND: With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately. RESULTS: Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of previously phenotyped individuals, thereby enabling an efficient incremental strategy. The effective performance of SPARE is demonstrated on simulated data as well as on an experimental yeast dataset. CONCLUSIONS: Using entropy reduction as an objective criterion gives a natural way to tackle both issues of detection and localization simultaneously and to integrate intermediate phenotypic data. We foresee entropy-based strategies as a fruitful research direction for selective phenotyping

The time course of cancer detection performance

The purpose of this study was to measure how mammography readers' performance varies with time of day and time spent reading. This was investigated in screening practice and when reading an enriched case set. In screening practice records of time and date that each case was read, along with outcome (whether the woman was recalled for further tests, and biopsy results where performed) was extracted from records from one breast screening centre in UK (4 readers). Patterns of performance with time spent reading was also measured using an enriched test set (160 cases, 41% malignant, read three times by eight radiologists). Recall rates varied with time of day, with different patterns for each reader. Recall rates decreased as the reading session progressed both when reading the enriched test set and in screening practice. Further work is needed to expand this work to a greater number of breast screening centres, and to determine whether these patterns of performance over time can be used to optimize overall performance

Accuracy evaluation of radiographers screen reading mammograms

This thesis evaluated the accuracy of radiographers screen-reading mammograms. This was undertaken as a potential solution to current radiologist workforce shortages that may contribute to delays in women receiving their screening mammogram results. This large, well-designed Australian study undertook extensive analysis and imparts evidence that even prior to any formal reading training, radiographers have good accuracy levels when screen-reading mammograms. It is expected that with formal screen-reading training these accuracy levels will further improve, such that radiographers have the potential to be one of the two screen-readers within the BreastScreen Australia program, contributing to timeliness and improved accuracy outcomes

Fatigue in radiology : a fertile area for future research

Fatigue in radiologists may be responsible for a large number of medical errors. This review describes the latest research on fatigue in radiology. This includes measurement methods, and recent evidence on how fatigue affects accuracy in laboratory test conditions and in clinical practice. The extensive opportunities for future research in the area are explored, including testing interventions to reduce fatigue-related error, and further understanding of which fatigue measures correlate with errors. Finally we explore the possibility of answering these questions using large population based observational studies and pragmatic integrated randomised controlled trials

Effect of Using the Same vs Different Order for Second Readings of Screening Mammograms on Rates of Breast Cancer Detection A Randomized Clinical Trial

Importance Interpreting screening mammograms is a difficult repetitive task that can result in missed cancers and false-positive recalls. In the United Kingdom, 2 film readers independently evaluate each mammogram to search for signs of cancer and examine digital mammograms in batches. However, a vigilance decrement (reduced detection rate with time on task) has been observed in similar settings. Objective To determine the effect of changing the order for the second film reader of batches of screening mammograms on rates of breast cancer detection. Design, Setting, and Participants A multicenter, double-blind, cluster randomized clinical trial conducted at 46 specialized breast screening centers from the National Health Service Breast Screening Program in England for 1 year (all between December 20, 2012, and November 3, 2014). Three hundred sixty readers participated (mean, 7.8 readers per center)—186 radiologists, 143 radiography advanced practitioners, and 31 breast clinicians, all fully qualified to report mammograms in the NHS breast screening program. Interventions The 2 readers examined each batch of digital mammograms in the same order in the control group and in the opposite order to one another in the intervention group. Main Outcomes and Measures The primary outcome was cancer detection rate; secondary outcomes were rates of recall and disagreements between readers. Results Among 1 194 147 women (mean age, 59.3; SD, 7.49) who had screening mammograms (596 642 in the intervention group; 597 505 in the control group), the images were interpreted in 37 688 batches (median batch size, 35; interquartile range [IQR]; 16-46), with each reader interpreting a median of 176 batches (IQR, 96-278). After completion of all subsequent diagnostic tests, a total of 10 484 cases (0.88%) of breast cancer were detected. There was no significant difference in cancer detection rate with 5272 cancers (0.88%) detected in the intervention group vs 5212 cancers (0.87%) detected in the control group (difference, 0.01% points; 95% CI, −0.02% to 0.04% points; recall rate, 24 681 [4.14%] vs 24 894 [4.17%]; difference, −0.03% points; 95% CI, −0.10% to 0.04% points; or rate of reader disagreements, 20 471 [3.43%] vs 20 793 [3.48%]; difference, −0.05% points; 95% CI, −0.11% to 0.02% points). Conclusions and Relevance Interpretation of batches of mammograms by qualified screening mammography readers using a different order vs the same order for the second reading resulted in no significant difference in rates of detection of breast cancer. Trial Registration isrctn.org Identifier: ISRCTN4660337

Improving Workflow Efficiency for Mammography Using Machine Learning.

OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. METHODS: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient's nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. RESULTS: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. CONCLUSION: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy

Patients’ Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients’ attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients’ perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists’ expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics

Recall Rates in Screening Mammography: Variability in Performance and Decisions

Having a high recall rate may increase the probability of cancer being detected earlier, however it also has been related to increased false positive decisions, causing significant psychological and economical costs for both screened women and the mammography screening service. Therefore, the purpose of this thesis is to explore the impact of various recall rates on breast radiologists’ performance in a laboratory setting. Methods This study was designed to encompass two aspects 1) the effect of setting varying recall rates on the performance of breast radiologists in screening mammography 2) types of mammographic appearances of breast cancer are more likely to be missed at different recall rates. Five Australian breast radiologists were recruited to read one single test set of 200 mammographic cases (180 normal and 20 abnormal cases) over three different recall rate conditions: free recall, 15% and 10%. These radiologists were tasked with marking the location of suspicious lesions and providing a confidence. Results A significant decrease in radiologists’ performance was observed when reading at lower recall rates, with lower sensitivity (P=0.002), case location sensitivity (P=0.002) and ROC AUC (P=0.003). Reading at a lower recall rate had a significant increase in specificity (P=0.002). The second study of this thesis showed that breast radiologists demonstrated lower sensitivity and receiver ROC AUC for non-specific density (NSD) (P=0.04 and P=0.03 respectively) and mixed features (P=0.01 and P=0.04 respectively) when reading at 15% and 10% recall rates. No significant change was observed on cancer characterized with stellate masses (P=0.18 and P=0.54 respectively) and architectural distortion (P=1.00 and P=0.37 respectively). Conclusion Reducing the number of recalled cases to 10% significantly reduced breast radiologists’ performance. Stellate masses were likely to be recalled (90.0%) while NSDs were likely to be missed (45.6%) at reduced recall rates