Imaging Pearls ❯ Chest ❯ Breast Cancer
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- Background:The Personal Performance in Mammographic Screening (PERFORMS) scheme is used to assess reader performance. Whether this scheme can assess the performance of artificial intelligence (AI) algorithms is unknown.
Purpose:To compare the performance of human readers and a commercially available AI algorithm interpreting PERFORMS test sets.
Materials and Methods:In this retrospective study, two PERFORMS test sets, each consisting of 60 challenging cases, were evaluated by human readers between May 2018 and March 2021 and were evaluated by an AI algorithm in 2022. AI considered each breast separately, assigning a suspicion of malignancy score to features detected. Performance was assessed using the highest score per breast. Performance metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), were calculated for AI and humans. The study was powered to detect a medium-sized effect (odds ratio, 3.5 or 0.29) for sensitivity - Results:A total of 552 human readers interpreted both PERFORMS test sets, consisting of 161 normal breasts, 70 malignant breasts, and nine benign breasts. No difference was observed at the breast level between the AUC for AI and the AUC for human readers (0.93% and 0.88%, respectively; P = .15). When using the developer’s suggested recall score threshold, no difference was observed for AI versus human reader sensitivity (84% and 90%, respectively; P = .34), but the specificity of AI was higher (89%) than that of the human readers (76%, P = .003). However, it was not possible to demonstrate equivalence due to the size of the test sets. When using recall thresholds to match mean human reader performance (90% sensitivity, 76% specificity), AI showed no differences inperformance, with a sensitivity of 91% (P =. 73) and a specificity of 77% (P = .85).
Conclusion:Diagnostic performance of AI was comparable with that of the average human reader when evaluating cases from two enriched test sets from the PERFORMS scheme. - Conclusion: Diagnostic performance of AI was comparable with that of the average human reader when evaluating cases from two enriched test sets from the PERFORMS scheme.
Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme.
Chen Y, Taib AG, Darker IT, James JJ.
Radiology. 2023 Sep;308(3):e223299. doi: 10.1148/radiol.223299. PMID: 37668522. - Key Results
■No difference in performance was observed between artificial intelligence (AI) and 552 human readers in the detection of breast cancer in 120 examinations from two Personal Performance in Mammographic Screening test sets (area under the receiver operating characteristic curve, 0.93 and 0.88, respectively; P = .15).
■When using AI score recall thresholds that matched mean human reader performance (90% sensitivity, 76% specificity), AI showed no difference in sensitivity (91%, P = .73) or specificity (77%, P = .85) compared with human readers.Figure 1:Flow diagram shows human reader inclusion and exclusion criteria. NHSBSP = National Health Service Breast Screening Programme, PERFORMS =
Personal Performance in Mammographic Screening Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme.
Chen Y, Taib AG, Darker IT, James JJ.
Radiology. 2023 Sep;308(3):e223299. doi: 10.1148/radiol.223299. PMID: 37668522. - “The AI algorithm used was a commercially available product (Lunit INSIGHT MMG, version 1.1.7.1; Lunit). All images were analyzed using Lunit software installed on the author’s (Y.C.) local server at the University of Nottingham. Lunit had no access to the cases before, during, or after the study. AI acted as an independent reader of cases. The Lunit AI algorithm provided scores that rated suspicion of malignancy against each feature detected on a scale of 0 (low) to 100 (high). The highest rating given to a feature detected within each breast was taken as the overall score for that breast and compared with the ground truth. As each breast was scored separately, an abnormality requiring re-call had to be localized to the correct breast by AI (Fig 2). When no features of interest were detected, a breast-level score of zero was assigned “
Personal Performance in Mammographic Screening Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme.
Chen Y, Taib AG, Darker IT, James JJ.
Radiology. 2023 Sep;308(3):e223299. doi: 10.1148/radiol.223299. PMID: 37668522. - “Diagnostic performance of a commercially available arti-ficial intelligence (AI) algorithm was comparable with that of human readers when evaluating cases from two enriched test sets of the Personal Performance in Mammographic Screening (PERFORMS) scheme. The use of external quality assessment schemes like PERFORMS may provide a model for regularly assessing the performance of AI in a way similar to the monitor-ing of human readers, but further work is needed to ensure this assessment model could work for other AI algorithms, screening populations, and readers.”
Personal Performance in Mammographic Screening Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme.
Chen Y, Taib AG, Darker IT, James JJ.
Radiology. 2023 Sep;308(3):e223299. doi: 10.1148/radiol.223299. PMID: 37668522. - “Double reading is recommended by European guide-lines to optimize mammographic sensitivity and is com-mon practice in the United Kingdom (5). Shortages in qualified readers worldwide make double reading an un-sustainable burden, for which AI is a logical solution. The results of this study suggest that AI could confidently act as a second reader to decrease workloads. While double read-ing has generally not been used in the United States, many U.S. radiologists interpreting mammograms are nonspecialized and do not read high volumes of mammograms. Thus, the AI system evaluated by Chen et al could be used as a supplemental tool to aid the performance of readers in the United States or in other countries where screening programs use a single reading.”
The Days of Double Reading Are Numbered: AI Matches Human Performance for Mammography Screening.
Philpotts L.
Radiology. 2023 Sep;308(3):e232034. doi: 10.1148/radiol.232034. PMID: 37668520. - “Mammogram interpretation remains one of the most areas of radiology and is as much an art as it is a science. The adaptation of AI is also proving to be a finely crafted art. The development and incorporation of AI into breast imaging practice has and will continue to be challenging. The precise role of AI is still to be determined; however, studies such as the one conducted by Chen et al will help move the field in a positive direction. As a second reader, it appears AI has a definite role that should ease the demanding job of reading large volumes of screening mammograms.”
The Days of Double Reading Are Numbered: AI Matches Human Performance for Mammography Screening.
Philpotts L.
Radiology. 2023 Sep;308(3):e232034. doi: 10.1148/radiol.232034. PMID: 37668520.
- ”AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up.”
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study
Kristina Lång et al.
Lancet Oncol 2023; 24: 936–44 - “The results from this randomised trial support the findings of earlier retrospective studies, indicating a general potential of AI to improve screening efficacy and reduce workload. The clinical safety analysis concludes that the AI-supported screen-reading procedure can be considered safe. Implementation of AI in clinical practice to reduce the screen-reading workload could therefore be considered to help address workforce shortages. The assessment of the primary endpoint of interval cancer rate, together with a characterisation of detected cancers in the entire study population, will provide further insight into the efficacy of screening, possible side-effects such as overdiagnosis, and the prognostic implications of using AI in mammography screening, taking cost-effectiveness into account.”
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study
Kristina Lång et al.
Lancet Oncol 2023; 24: 936–44 - To our knowledge, this is the first randomised controlled trial investigating the use of AI in mammography screening. In this first report, the objective was to assess the safety of an AI-supported screen-reading procedure, involving triage and detection support. AI-supported screening resulted in 20% more cancers being detected and exceeded the lowest acceptable limit for safety compared with standard double reading without AI, without affecting the false positive rate. The AI supported screen-reading procedure enabled a 44·3% reduction in the screen-reading workload. The results indicate that the proposed screening strategy is safe.
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study
Kristina Lång et al.
Lancet Oncol 2023; 24: 936–44 - “In summary, this clinical safety analysis of the MASAI trial, in which an AI system was used to triage screening examinations to single or double reading and as detection support, showed that AI-supported mammography screening can be considered safe, since it resulted in a similar rate of screen-detected cancer—exceeding the lowest acceptable limit for safety—without increasing rates of recalls, false positives, or consensus meetings, and while substantially reducing the screen-reading workload compared with screening by means of standard double reading.”
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study
Kristina Lång et al.
Lancet Oncol 2023; 24: 936–44
- Purpose: To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk.
Materials and Methods: This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC).
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study
Vignesh A. Arasu et al.
Radiology 2023; 307(5):e222733 - Results: The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66–0.68; Bonferroni-adjusted P < .0016).
Conclusion: When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction.
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study Vignesh
A. Arasu et al.
Radiology 2023; 307(5):e222733 - Summary
“Negative screening mammographic examinations were analyzed with five artificial intelligence (AI) algorithms; all predicted breast cancer risk to 5 years better than the Breast Cancer Surveillance Consortium (BCSC) clinical risk model, and combining AI and BCSC models further improved prediction.”
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study Vignesh
A. Arasu et al.
Radiology 2023; 307(5):e222733 - Key Results
■ Five artificial intelligence (AI) algorithms were used to generate continuous risk scores from retrospectively acquired screening mammographic examinations negative for cancer in 18 019 women.
■ AI predicted incident cancers at 0 to 5 years better than the Breast Cancer Surveillance Consortium (BCSC) clinical risk model (AI time-dependent area under the receiver operating characteristic curve [AUC] range, 0.63–0.67; BCSC time-dependent AUC, 0.61; Bonferroni-adjusted P < .0016).
■ Combining AI algorithms with BCSC slightly improved the time dependent AUC versus AI alone (AI with BCSC time-dependent AUC range, 0.66–0.68; Bonferroni-adjusted P < .0016).
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study
Vignesh A. Arasu et al.
Radiology 2023; 307(5):e222733 - “Mammography AI algorithms provide an approach for improving breast cancer risk prediction beyond clinical variables such as age, family history, or the traditional imaging risk biomarker of breast density. The absolute increase in the AUC for the best mammography AI relative to BCSC was 0.09 for interval cancer risk and 0.06 for overall 5-year risk, a substantial and clinically meaningful improvement. The overall performance improvement remained when restricting the analysis to invasive cancer only. In order for an AI model to achieve an AUC of approximately 0.7, the model must have predictors that are two to three times more informative than clinical models such as the BCSC with an AUC of approximately 0.6 (1)”
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study
Vignesh A. Arasu et al.
Radiology 2023; 307(5):e222733 - “In conclusion, mammography artificial intelligence (AI) algorithms provided prediction of breast cancer risk to 5 years that was better than the Breast Cancer Surveillance Consortium (BCSC) clinical risk model, and the combination of AI and BCSC models further improved prediction. Our results imply that mammography AI algorithms alone may provide a clinically meaningful improvement compared with current clinical risk models at early time horizons (ie, time during which risk is assessed), with further improvements in prediction when AI and clinical risk models are combined. Although AI algorithm performance declines with longer time horizons, most of the algorithms evaluated have not yet been trained to predict longer-term outcomes, suggesting a rich opportunity for further improvement.”
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study
Vignesh A. Arasu et al.
Radiology 2023; 307(5):e222733
- "In summary, AI has emerged as a promising tool with the potential to impact how radiologists, and even nonradiologists, provide timely care to patients presenting with symptoms, especially those in lower-resource environments. These AI tools are accurate when classifying malignant and benign breast masses and, therefore, can streamline both workflow and the allocation of downstream care. With continued improvements in portable scanning equipment, further education of on-site nonradiologist providers, and continued advances of existing AI algorithms, it appears that AI is primed to revolutionize how we care for patients presenting with breast complaints. Integrating AI into these lower-resource environments is yet another step that will lessen the burden of disease in these vulnerable patients who currently face major obstacles to care. The hope is that such steps will ultimately translate into lower mortality and better overall outcomes.”
The Promise of AI in Advancing Global Radiology
Priscilla J. Slanetz
Radiology 2023; 00:e230895 - “Breast cancer poses a global public health threat, with an estimated 2.3 million cases in 2020 and predictions that there will be more than 3 million cases by 2040 (1). Although breast cancer mortality has decreased due to advances in early detection and diagnosis, approximately 685 000 women still annually die of the disease. A greater proportion of these deaths affect women living in low- and middle-income countries. Although such lower-resource environments face many challenges, one of the greatest problems is the lack of integrated health infrastructure that provides timely access to preventative screening and subsequent surgical and oncologic care. Although there have been many technological advances over the past few decades, mammographic screening is not commonplace in these countries, and, therefore, unlike in the United States, most women with breast cancer present with a palpable mass and often locally advanced disease. This presentation, coupled with a relative lack of access to state-of-the-art oncologic care, translates into higher morbidity and mortality.” from the disease.
The Promise of AI in Advancing Global Radiology
Priscilla J. Slanetz
Radiology 2023; 00:e230895 - “The results show that inexperienced, moderately experienced, and very experienced radiologists reading mammograms are prone to automation bias when being supported by an AI-based system. This and other effects of human and machine interaction must be considered to ensure safe deployment and accurate diagnostic performance when combining human readers and AI.”
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - Background: Automation bias (the propensity for humans to favor suggestions from automated decision-making systems) is a known source of error in human-machine interactions, but its implications regarding artificial intelligence (AI)–aided mammography reading are unknown.
Conclusion: The results show that inexperienced, moderately experienced, and very experienced radiologists reading mammograms are prone to automation bias when being supported by an AI-based system. This and other effects of human and machine interaction must be considered to ensure safe deployment and accurate diagnostic performance when combining human readers and AI.
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - “ The percentage of correctly rated mammograms by inexperienced, moderately experienced , and very experienced radiologists was significantly impacted by the correctness of the AI prediction of BI-RADS category. Inexperienced radiologists were significantly more likely to follow the suggestions of the purported AI when it incorrectly suggested a higher BI-RADS category than the actual ground truth compared with both moderately and very experienced readers.”
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - “Several studies have shown a synergistic combination of radiologists and AI is possible. However, there is also the danger that radiologists may stop critically engaging with the AI results and start mindlessly following them. This overreliance on a decision support system, known as automation bias, has been observed in a wide range of fields, such as aviation, engineering, and medicine, and a long line of research has focused on the conditions that cause automation bias to arise.”
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - Summary
Incorrect advice by a purported artificial intelligence–based decision support system impaired the performance of radiologists with varying levels of expertise, ranging from inexperienced to very experienced, when reading mammograms.
Key Results
■ In this prospective experiment, 27 radiologists who interpreted 50 mammograms with the assistance of a purported artificial intelligence (AI)–based system were significantly affected by incorrect suggestions from the system.
■ Inexperienced radiologists were more likely to follow the suggestions of the AI system when it incorrectly suggested a higher Breast Imaging Reporting and Data System category compared with moderately (mean degree of bias, 4.0 +/- 1.8 vs 2.4 +/- 1.5; P = .044; r = 0.46) and very (mean degree of bias, 4.0 +/- 1.8 vs 1.2 +/- 0.8; P = .009; r = 0.65) experienced readers.
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - “Overall, our results show that automation bias can affect the performance of radiologists regardless of their experience. Therefore, specific strategies should be considered to mitigate automation bias. Previous research has shown that presenting users with the confidence levels of the decision support system can help reduce automation bias by keeping users more critically engaged with the output of the system. In the case of an AI-based system, this could be implemented by displaying the probability of each output. Another strategy to mitigate automation bias involves educating users about the reasoning process of the decision support system .”
Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance
Thomas Dratsch et al.
Radiology 2023; 000:1–9 - AI and User Experience
- AI has the potential to make everyone “an expert”
- AI has the potential to increase error if the program is not accurate as less experienced users are more apt to trust AI - AI and Early Cancer Detection
- Breast cancer
- Pancreas cancer
- Colon cancer
- Lung cancer
- Liver cancer - Purpose To compare the usefulness of multi-phase liver CT and single-phase abdominopelvic CT (APCT) in evaluating liver metastasis in newly diagnosed breast cancer patients.
Conclusion Multi-phase liver CT has little benefit over single-phase APCT in assessing liver metastasis in patients with breast cancer.
Evaluation of liver metastasis in patients with breast cancer: Comparison of single‑phase abdominopelvic CT and multi‑phase liver CT
Seong Eun Ko · Kyoung Doo Song · Dong Ik Cha
Abdominal Radiology (2023) 48:1320–1328 - “In conclusion, the overall liver metastasis rate was as low as 0.6% at initial staging of breast cancer patients and multiphase liver CT has little benefit in assessing liver metastasis than single-phase APCT. Considering the radiation dose, it would be desirable to use single-phase APCT for initial staging workup in patients with breast cancer.”
Evaluation of liver metastasis in patients with breast cancer: Comparison of single‑phase abdominopelvic CT and multi‑phase liver CT
Seong Eun Ko · Kyoung Doo Song · Dong Ik Cha
Abdominal Radiology (2023) 48:1320–1328
- “In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.”
International evaluation of an AI system for breast cancer screening
Scott Mayer McKinney et al
Nature | Vol577 | 2January2020 - Objectives: Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist.
Conclusions: Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system.
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
van Winkel SL et al
Eur Radiol. 2021 Nov;31(11):8682-8691. - • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time.
• The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams.
• The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
van Winkel SL et al
Eur Radiol. 2021 Nov;31(11):8682-8691.
- PURPOSE:To (a) perform a pilot study comparing radiologists' reading of breast density at computed tomography (CT) of the chest with breast density readings from mammography performed in the same patient and (b) compare a subset of these with computer-derived measurements of breast density at CT.
CONCLUSION :Preliminary results suggest that on further validation, breast density readings at CT may provide important additional risk information on CT of the chest and that computer-derived measurements may be helpful in such assessment. Breast density: comparison of chest CT with mammography.
Salvatore M et al.
Radiology. 2014 Jan;270(1):67-73. - Advances in Knowledge
■ Breast density readings at CT are consistent with mammographic breast density readings (κ values for radiologist 1 vs 2 were 0.58 and 0.54, respectively), and there is greater interobserver reader agreement at CT compared with mammography (κ = 0.79 vs 0.62, respectively).
■ Computer-generated CT measures of breast density are consistent with CT breast density readings: 36 of 40 (90%) were correctly classified.
Implication for Patient Care
■ CT breast density readings represent an opportunity to provide additional information about the risk of breast cancer that is readily available and currently not being used in a standardized manner.
Breast density: comparison of chest CT with mammography.
Salvatore M et al.
Radiology. 2014 Jan;270(1):67-73. 
- “The IMA arises from the first portion of the subclavian artery and immediately passes downwards close to the pleura within the upper intercostal space. Further distal, it proceeds anteriorly to the transversus thoracic muscle to end in the sixth intercostal space by dividing into the superior epigastric and musculophrenic artery.”
Idiopathic internal mammary artery aneurysm
Jens Heyn et al.
J Surg Case Rep. 2014 Dec; 2014(12): rju125 - “Aneurysms of the internal mammary artery (IMAA) are uncommon clinical entities and usually occur in patients after sternotomy, placement of a central venous catheter or pacemaker leads. Less common, these aneurysms are associated with vasculitis (e.g. Kawasaki disease), connective tissue disorders (e.g. Marfan syndrome), chest wall infections or atherosclerosis. However, changes in the structure of the vascular wall at the cellular level such as cystic medial necrosis or hyperplasia lead to loss of elasticity and formation of aneurysms.”
Idiopathic internal mammary artery aneurysm
Jens Heyn et al.
J Surg Case Rep. 2014 Dec; 2014(12): rju125 - “The internal mammary arteries (IMAs) are commonly used as the conduit to bypass major coronary artery stenosis, and have shown greater long-term patency rates and improved survival as compared to saphenous vein grafts (SVGs). The benefit of IMAs over SVGs on mortality has been consistently observed irrespective of age, gender, degree of luminal stenosis in the left main coronary artery or preoperative left ventricular function with the survival differences widening over time . The main differences are related to the development of atherosclerosis which has rarely been observed in the IMA graft while it develops at a fairly rapid rate in the SVG.”
Why is the mammary artery so special and what protects it from atherosclerosis?
Fumiyuki Otsuka, Kazuyuki Yahagi, Kenichi Sakakura, Renu Virmani
Ann Cardiothorac Surg 2013;2(4):519-526
- Summary
A deep learning algorithm was used to assess mammographic breast density at the level of an experienced mammographer during routine clinical practice.
Implications for Patient Care:
* A deep learning algorithm was used to reliably and accurately assess mammographic breast density in a large clinical practice.
* Given the high level of agreement between the deep learning algorithm and experienced mammographers, this algorithm has the potential to standardize and automate routine breast density assessment.
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman CD et al.
Radiology 2019; 290:52–58 - “Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in unnecessary procedures. To address this issue, we developed a DL model to assess mammographic breast density that was trained by using the assessments of experienced breast imagers. Our DL model was deployed in the mammography clinic to assess performance and acceptance in a large academic breast imaging practice. In this setting, the DL model density assessment was accepted as the final reading in 90% of mammograms by an experienced breast imager.”
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman CD et al.
Radiology 2019; 290:52–58 - “Also, this model was trained on mammograms at one academic center that used mammography units from one vendor (Hologic), and further testing on diverse mammograms acquired with machines from multiple vendors and from different institutions is needed. Finally, during the clinical implementation of our project, acceptance of the DL density assessment was measured in an unblinded manner."
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman CD et al.
Radiology 2019; 290:52–58 - In summary, we present an analysis of clinical implementation of a DL model used to assess breast density in women undergoing screening digital mammography. Our DL model provides efficient and reliable density assessments, both at the patient level and at the population level, and it is designed to be widely available, simple to use, and cost effective. It can be used to measure breast density in a diverse set of patients, without limitations based on prior surgery or other breast interventions. Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman CD et al.
Radiology 2019; 290:52–58 - “Our tool can potentially address concerns for current breast density legislation, and it can help providers supply more accurate information to patients and help health systems optimize the use of supplemental screening resources. To this end, we have made our tool publicly available for research use at http://learningtocure. csail.mit.edu.
Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
Lehman CD et al.
Radiology 2019; 290:52–58
- “Multidetector CT and dynamic contrast en- hanced techniques have been used to study features of malignancy in breast tumors. Ir- regular margins, irregular shape, and rim en- hancement are the most highly predictive features for malignancy in these studies. A spiculated and irregular margin is the most accurate sign for malignancy.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “A washout pattern on postcontrast images had high positive predictive value and sensitivity, although low negative predictive value and specificity. Diffuse regional en- hancement is also shown to have high positive predictive value for malignancy.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “At CT, invasive ductal carcinoma appears as a dense, spiculated mass with marked early and/or peripheral enhancement. The presence of rim enhancement and internal enhancing septations can be suggestive signs. In advanced cases, associated skin thickening, lymphadenopathy, or pleural effusions may be seen.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “Inflammatory carcinoma is an uncommon, aggressive tumor with early dermal lymphatic invasion and poor prognosis. Clinical diagnosis is based on increased warmth, induration of breast skin, erysipeloid edge (peau d’orange), and nipple retraction. In some cases, inflammatory carcinoma may be indistinguishable from mastitis and abscess but fails to respond to antibiotics. Inflammatory carcinoma should be considered in the differential diagnosis when breast edema is accompanied by clinical signs of infection.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “Breast hematomas and seromas can be seen after biopsy, trauma, or surgery . Their diagnosis can be made by correlating the finding to the clinical history. Immediately after surgery or biopsy, the surrounding edema may obscure a hematoma. Hematomas will become smaller over time and eventually resorb and therefore can be
distinguished from other masses.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “If the scar can be shown to occupy a surgical site, then the spiculated appearance is not of concern. In differentiating a scar from cancer, correlating prior biopsy locations from the patient history, reviewing prior images, and noting linear scar markers in the locations of prior biopsy are very important. Masses not corresponding to a postbiopsy scar should be considered suspicious. In addition, any new tissue growth in a previously identified postoperative scar (particularly after cancer resection) should be viewed with suspicion.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “In general, larger round or oval calcifications that are uniform in size and shape have a higher probability of being associated with a benign process, whereas smaller, irregular, polymorphic, clustered calcifications heterogeneous in size and morphology are more often associated with a malignant process. Nearly all calcifications currently seen at CT are benign, on the basis of size alone, due to the limited spatial resolution .” Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51
- “CT is very sensitive for the detection of coarse calcifications. When calcifications are identified in the breast at CT, they are nearly all benign. They should be characterized when resolution allows as lucent-centered calcifications, eggshell or rim calcifications, coarse or popcornlike calcifications, large rodlike calcifications, or round calcifications.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51 - “In our experience, breast abnormalities at CT are frequently overlooked or inaccurately assessed. Our aim has been to expose the general radiologist to the imaging characteristics of a range of breast abnormalities in addition to providing a succinct and accurate method of describing and classifying these findings. It is important that general radiologists pay attention to the breasts on CT scans and that they are trained to recognize and report abnormal findings with confidence.”
Breast Lesions Incidentally Detected with CT: What the General Radiologist Needs to Know Harish MG et al. RadioGraphics 2007; 27:S37–S51
CT of Breast Cancer
Pulmonary metastases occur in 30% of all malignancies, usually through hematogenous dissemination. In 10-25% of cases, the lungs are the only site of metastasis. The classic CT appearance is that of multiple well-defined nodules, often seen in continuity with an underlying blood vessel. Lung metastases are typically widespread, although solitary metastases are not at all uncommon, particulary in colon, kidney, breast, testicular, and musculoskeletal primaries.CT of Breast Cancer: facts
Internal mammary adenopathy is an important site of occult metastasis in breast cancer. Involvement is often ipsilateral to the primary tumor and represents a site of regional nodal spread, much like the axilla. Lymphoma represents the other common malignancy to selectively target this nodal chain. Involvement is usually the result of contiguous spread from the anterior mediastinal or paratracheal area to the other mediastinal lymph node groups, including the internal mammary chain. Occasionally, isolated internal mammary nodal involvement is seen.- Dynamic CT-mammography: CT Protocol
- 100 cc of non-ionic contrast injection at 3 cc/sec
- Scans were acquired at 30 sec and 2 minutes post start of injection
- Images reviewed with axial CT and MIP displays - "Multiple enhancing lesions on CT-mammography in patients with breast cancer were relatively common, and most of them represented multiple cancer lesions. Dynamic CT-mammography is potentially useful in evaluating the spread of breast cancer.”
Multiple Enhancing Lesions Detected on Dynamic Helical Computed Tomography-Mammography
Nishino M et al.
J Comput Assit Tomogr 2003; 27:771-778 - “The CT features of breast lesions are less familiar than are those of mammography and ultrsonography; however, CT can demonstrate many abnormal breast conditions. Also, some breast lesions are better visualized on CT than on mammography.”
Computed Tomography of the Breast: Abnormal Findings with Mammographic and Sonographic Correlation
Kim SM et al.
J Comput Assist Tomogr 2003;27:761-770 - MDCT of Breast Cancer: CT Appearance
- Spiculated and Irregular margin
- Irregular shape
- Rim enhancement - "Administration of IV contrast material by means of hand injection led to damage of four central venous catheters during a 6 yr period at our institution (0.3% of central venous catheters hand injected during that time)."
Is Hand Injection of Central Venous catheters for Contrast Enhanced CT Safe for Children
Donnelly LF et al.
AJR 2007;189:1530-1532 - "Dedicated breast CT is currently investigational but may eventually have applications in screening or diagnostic evaluation for breast cancer, as a more accessible replacement for breast MR imaging or as a guidance method for robotic breast biopsy or tumor ablation procedures."
Dedicated Breast CT: Initial Clinical Experience Lindfors KK et al.
Radiology 2008;246:725-733 - "Subjects found CT significantly more comfortable than mammography."
Dedicated Breast CT: Initial Clinical Experience Lindfors KK et al.
Radiology 2008;246:725-733 - "Overall, CT was equal to mammography for visualization of breast lesions.Breast CT was significantly better than mammography for visualization of masses; mammography outperformed CT for visualization of microcalcifications. No significant differences between CT and mammography were seen among benign versus malignant lesions or for effect of breast density on lesion visualization."
Dedicated Breast CT: Initial Clinical Experience Lindfors KK et al.
Radiology 2008;246:725-733