Deep learning in breast cancer screening

Breast cancer is the most common cancer form among women worldwide and the incidence is rising. When mammography was introduced in the 1980s, mortality rates decreased by 30% to 40%. Today all women in Sweden between 40 to 74 years are invited to screening every 18 to 24 months. All women attending screening are examined with mammography, using two views, the mediolateral oblique (MLO) view and the craniocaudal (CC) view, producing four images in total. The screening process is the same for all women and based purely on age, and not on other risk factors for developing breast cancer. Although the introduction of population-based breast cancer screening is a great success, there are still problems with interval cancer (IC) and large screen detected cancers (SDC), which are connected to an increased morbidity and mortality. To have a good prognosis, it is important to detect a breast cancer early while it has not spread to the lymph nodes, which usually means that the primary tumor is small. To improve this, we need to individualize the screening program, and be flexible on screening intervals and modalities depending on the individual breast cancer risk and mammographic sensitivity. In Sweden, at present, the only modality in the screening process is mammography, which is excellent for a majority of women but not for all. The major lack of breast radiologists is another problem that is pressing and important to address. As their expertise is in such demand, it is important to use their time as efficiently as possible. This means that they should primarily spend time on difficult cases and less time on easily assessed mammograms and healthy women. One challenge is to determine which women are at high risk of being diagnosed with aggressive breast cancer, to delineate the low-risk group, and to take care of these different groups of women appropriately. In studies II to IV we have analysed how we can address these challenges by using deep learning techniques. In study I, we described the cohort from which the study populations for study II to IV were derived (as well as study populations in other publications from our research group). This cohort was called the Cohort of Screen Aged Women (CSAW) and contains all 499,807 women invited to breast cancer screening within the Stockholm County between 2008 to 2015. We also described the future potentials of the dataset, as well as the case control subset of annotated breast tumors and healthy mammograms. This study was presented orally at the annual meeting of the Radiological Society of North America in 2019. In study II, we analysed how a deep learning risk score (DLrisk score) performs compared with breast density measurements for predicting future breast cancer risk. We found that the odds ratios (OR) and areas under the receiver operating characteristic curve (AUC) were higher for age-adjusted DLrisk score than for dense area and percentage density. The numbers for DLrisk score were: OR 1.56, AUC, 0.65; dense area: OR 1.31, AUC 0.60, percent density: OR 1.18, AUC, 0.57; with P < .001 for differences between all AUCs). Also, the false-negative rates, in terms of missed future cancer, was lower for the DLrisk score: 31%, 36%, and 39% respectively. This difference was most distinct for more aggressive cancers. In study III, we analyzed the potential cancer yield when using a commercial deep learning software for triaging screening examinations into two work streams – a ‘no radiologist’ work stream and an ‘enhanced assessment’ work stream, depending on the output score of the AI tumor detection algorithm. We found that the deep learning algorithm was able to independently declare 60% of all mammograms with the lowest scores as “healthy” without missing any cancer. In the enhanced assessment work stream when including the top 5% of women with the highest AI scores, the potential additional cancer detection rate was 53 (27%) of 200 subsequent IC, and 121 (35%) of 347 next-round screen-detected cancers. In study IV, we analyzed different principles for choosing the threshold for the continuous abnormality score when introducing a deep learning algorithm for assessment of mammograms in a clinical prospective breast cancer screening study. The deep learning algorithm was supposed to act as a third independent reader making binary decisions in a double-reading environment (ScreenTrust CAD). We found that the choice of abnormality threshold will have important consequences. If the aim is to have the algorithm work at the same sensitivity as a single radiologist, a marked increase in abnormal assessments must be accepted (abnormal interpretation rate 12.6%). If the aim is to have the combined readers work at the same sensitivity as before, a lower sensitivity of AI compared to radiologists is the consequence (abnormal interpretation rate 7.0%). This study was presented as a poster at the annual meeting of the Radiological Society of North America in 2021. In conclusion, we have addressed some challenges and possibilities by using deep learning techniques to make breast cancer screening programs more individual and efficient. Given the limitations of retrospective studies, there is a now a need for prospective clinical studies of deep learning in mammography screening

Intersectional analysis of inequalities in self-reported breast cancer screening attendance using supervised machine learning and PROGRESS-Plus framework

BackgroundBreast cancer is a critical public health concern in Spain, and organized screening programs have been in place since the 1990s to reduce its incidence. However, despite the bi-annual invitation for breast cancer screening (BCS) for women aged 45–69, significant attendance inequalities persist among different population groups. This study employs a quantitative intersectional perspective to identify intersectional positions at risk of not undergoing breast cancer screening in Spain.MethodsWomen were selected from the 2020 European Health Interview Survey in Spain, which surveyed the adult population (&gt; 15 years old) living in private households (N = 22,072; 59% response rate). Inequality indicators based on the PROGRESS-Plus framework were used to disentangle existing social intersections. To identify intersectional groups, decision tree models, including classification and regression trees (CARTs), chi-squared automatic interaction detector (CHAID), conditional inference rees (CITs), and C5.0, along with an ensemble algorithm, extreme gradient boosting (XGBoost), were applied.ResultsXGBoost (AUC 78.8%) identified regional differences (Autonomous Community) as the most important factor for classifying BCS attendance, followed by education, age, and marital status. The C5.0 model (balanced accuracy 81.1%) highlighted that the relative importance of individual characteristics, such as education, marital status, or age, for attendance differs based on women’s place of residence and their degree of interaction. The highest risk of not attending BCS was observed among illiterate older women in lower social classes who were born in Spain, were residing in Asturias, Cantabria, Basque Country, Castile and León, Extremadura, Galicia, Madrid, Murcia, La Rioja, or Valencian Community, and were married, divorced, or widowed. Subsequently, the risk of not attending BCS extends to three other groups of women: women living in Ceuta and Melilla; single or legally separated women living in the rest of Spain; and women not born in Spain who were married, divorced, or widowed and not residing in Ceuta or Melilla.ConclusionThe combined use of decision trees and ensemble algorithms can be a valuable tool in identifying intersectional positions at a higher risk of not utilizing public resources and, thus, can aid substantially in developing targeted interventions to increase BCS attendance

Machine learning in oral squamous cell carcinoma: current status, clinical concerns and prospects for future-A systematic review

Background: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives: This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources: We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria: Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction: Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results: A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion: Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.Peer reviewe

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients