Impact of Digital Mammography on Cancer Detection and Recall Rates: 11.3 Million Screening Episodes in the English National Health Service Breast Cancer Screening Program.

Purpose To report the impact of changing from screen-film mammography to digital mammography (DM) in a large organized national screening program. Materials and Methods A retrospective analysis of prospectively collected annual screening data from 2009-2010 to 2015-2016 for the 80 facilities of the English National Health Service Breast Cancer Screening Program, together with estimates of DM usage for three time periods, enabled the effect of DM to be measured in a study of 11.3 million screening episodes in women aged 45-70 years (mean age, 59 years). Regression models were used to estimate percentage and absolute change in detection rates due to DM. Results The overall cancer detection rate was 14% greater with DM (P < .001). There were higher rates of detection of grade 1 and 2 invasive cancers (both ductal and lobular), but no change in the detection of grade 3 invasive cancers. The recall rate was almost unchanged by the introduction of DM. At prevalent (first) screening episodes for women aged 45-52 years, DM increased the overall detection rate by 19% (P < .001) and for incident screening episodes in women aged 53-70 years by 13% (P < .001). Conclusion The overall cancer detection rate was 14% greater with digital mammography with no change in recall rates and without confounding by changes in other factors. There was a substantially higher detection of grade 1 and grade 2 invasive cancers, including both ductal and lobular cancers, but no change in the detection of grade 3 invasive cancers. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by C.I. Lee and J.M. Lee in this issue

CAD in mammography: lesion-level versus case-level analysis of the effects of prompts on human decisions

Object: To understand decision processes in CAD-supported breast screening by analysing how prompts affect readers’ judgements of individual mammographic features (lesions). To this end we analysed hitherto unexamined details of reports completed by mammogram readers in an earlier evaluation of a CAD tool. Material and methods: Assessments of lesions were extracted from 5,839 reports for 59 cancer cases. Statistical analyses of these data focused on what features readers considered when recalling a cancer case and how readers reacted to CAD prompts. Results: About 13.5% of recall decisions were found to be caused by responses to features other than those indicating actual cancer. Effects of CAD: lesions were more likely to be examined if prompted; the presence of a prompt on a cancer increased the probability of both detection and recall especially for less accurate readers in subtler cases; lack of prompts made cancer features less likely to be detected; false prompts made non-cancer features more likely to be classified as cancer. Conclusion: The apparent lack of impact reported for CAD in some studies is plausibly due to CAD systematically affecting readers’ identification of individual features, in a beneficial way for certain combinations of readers and features and a damaging way for others. Mammogram readers do not ignore prompts. Methodologically, assessing CAD by numbers of recalled cancer cases may be misleading

Sexed up: theorizing the sexualization of culture

This paper reviews and examines emerging academic approaches to the study of ‘sexualized culture’; an examination made necessary by contemporary preoccupations with sexual values, practices and identities, the emergence of new forms of sexual experience and the apparent breakdown of rules, categories and regulations designed to keep the obscene at bay. The paper maps out some key themes and preoccupations in recent academic writing on sex and sexuality, especially those relating to the contemporary or emerging characteristics of sexual discourse. The key issues of pornographication and democratization, taste formations, postmodern sex and intimacy, and sexual citizenship are explored in detail. </p

Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study

Background: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. // Objective: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. // Methods: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. // Results: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. // Conclusions: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. // International Registered Report Identifier (IRRID): DERR1-10.2196/5056

Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study

Background: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. Objective: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. Methods: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. Results: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. Conclusions: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas

The number of women who would need to be screened regularly by mammography to prevent one death from breast cancer

The number of women who would need to be screened regularly by mammography to prevent one death from breast cancer depends strongly on several factors, including the age at which regular screening starts, the period over which it continues, and the duration of follow-up after screening. Furthermore, more women would need to be INVITED for screening than would need to be SCREENED to prevent one death, since not all women invited attend for screening or are screened regularly. Failure to consider these important factors accounts for many of the major discrepancies between different published estimates. The randomised evidence indicates that, in high income countries, around one breast cancer death would be prevented in the long term for every 400 women aged 50-70 years regularly screened over a ten-year period