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Improving Cancer Screening: How AI Empowers Radiologists

Cancer is a leading cause of global morbidity and mortality, with one in five people on average developing cancer in their lifetimes (The Burden of Cancer, n.d.). Cancer screening targets asymptomatic individuals and aims to identify either early-stage cancer or precancerous conditions. In many cases, this allows for timely intervention and improved treatment outcomes. In general, screening can be thought of as serving either a preventive or early detection role. Preventive screening aims to detect benign conditions that can turn cancerous, which is only possible with some cancers, while early detection approaches aim to detect early-stage cancer. Importantly, screening should not be considered a single test, but a process that includes identifying the target population, conducting diagnostic tests, and planning further work-up including treatment when necessary (World Health Organization. Regional Office for Europe, 2022).

Radiology has long played an essential role in determining the extent of local and distant tumor spread after a cancer diagnosis is made. However, it is also indispensable in the screening pathways of several common cancers. In these cases, medical imaging studies are either the primary screening tool or are used to decide on further work-up after screening using other methods, such as blood tests. Depending on the type of cancer, screening can involve medical imaging techniques such as mammography, computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound. National screening programs using medical imaging have been established for some of the most common cancers. Most of these programs target specific populations at risk of the specific cancer in question, identified using modifiable or non-modifiable risk factors.

Because cancer screening targets healthy people, it is especially essential that a screening program’s benefits outweigh its harms. This has to be carefully established for each program and is sometimes controversial (Lam et al., 2014). However, certain advantages and disadvantages to cancer screening apply to all screening techniques and cancers (Kramer, 2004; World Health Organization. Regional Office for Europe, 2022). Screening can reduce healthcare costs and improve patient quality of life. It also often improves the prognosis and treatment outcomes of people identified as having cancer and can provide reassurance to people in whom cancer is not found. However, sometimes early detection does not change the prognosis, and in these people, screening may instigate unnecessary treatment resulting in reduced health or quality of life. In fact, screening can sometimes detect cancers that would never have led to ill health or death in the person’s lifetime. In addition, false positives and false negatives are inevitable with any screening test. The former leads to overtreatment, with the resulting psychosocial and physical side effects, and the latter to false reassurance and delayed treatment.

Breast cancer

Breast cancer is the second leading cause of cancer deaths in women (Bray et al., 2018) and one of the most common cancers worldwide (Sung et al., 2021). Early detection and treatment can improve outcomes, and studies have shown up to 20% lower mortality in screened populations compared with populations not offered screening. Studies estimate that one breast cancer death is prevented on average for every 250 to 414 women screened (Marmot et al., 2013; Tabár et al., 2011). More than 100 countries worldwide have implemented large-scale breast cancer screening programs (Existence of National Screening Program for Breast Cancer, n.d.). The start of screening is recommended between ages 40 and 50 years (Ren et al., 2022) and is almost exclusively done using either mammography, which uses low-dose X-rays to image the breasts, or digital breast tomosynthesis, a similar technique that uses multiple projections to create a series of stacked images of the breast.


breast cancer ebook

The algorithm increased breast cancer detection by 12-27% by triaging mammograms that were assessed as negative after double-reading yet were considered suspicious by the algorithm for further assessment using MRI or ultrasound.

The accuracy of mammography varies considerably and even the most experienced radiologists’ readings have high false positive and false negative rates (Elmore et al., 2009; Lehman et al., 2015). It is estimated that at least one in three women screened will have a false positive mammography result during their lifetime (Castells et al., 2006). Mammography is particularly challenging in dense breasts (Boyd et al., 2007) and in women on hormone replacement therapy (Banks et al., 2006). Mammography screening is also a labor-intensive process. In many European countries, the standard of care is consensus double reading, in which two radiologists consecutively read each case and resolve disagreements by consensus (Giordano et al., 2012). There is unfortunately also a shortage of radiologists and radiographers specifically trained in mammography in many countries (Moran & Warren-Forward, 2012; Rimmer, 2017; Wing & Langelier, 2009).

Systems based on artificial intelligence (AI) have been incorporated at various stages in the breast cancer screening process. In a study of almost 30,000 women in the United States and the United Kingdom who received screening mammograms at intervals of 1–3 years and a follow-up period of up to 39 months, an ensemble of three deep-learning models was compared to histopathology and the interpretations of board-certified radiologists (McKinney et al., 2020). The algorithm had a 1.2–5.7 % higher specificity and a 2.7–9.4 % higher sensitivity compared to the radiologists who performed the first reading. The authors estimated that using the algorithm could render second readings unnecessary in up to 88 % of screening cases while maintaining accuracy, freeing up much-needed resources.

Consistently promising results have been reported in studies using AI-based systems in conjunction with radiologists. A study of almost 16,000 women receiving either digital mammography or digital breast tomosynthesis in Spain estimated that using a deeplearning-based algorithm would result in a 72.5% lower workload compared to double reading while maintaining sensitivity (Raya-Povedano et al., 2021). In this model, the least suspicious examinations would only be read by the algorithm and the top 2 % most suspicious examinations, as judged by the algorithm, would be flagged for further workup regardless of the radiologists’ interpretation. Similarly, a study of 7364 women in Sweden found that a commercially available deep-learning algorithm accurately classified the least suspicious mammograms, and these women underwent no further workup (Dembrower et al., 2020). This was achieved with a false negative rate of 0–2.6 %. The algorithm also increased breast cancer detection by 12–27 % by triaging mammograms that were assessed as negative after double-reading yet were considered suspicious by the algorithm for further assessment using MRI or ultrasound.

Other studies have used AI-based systems as a decision-referral step. In a study of over a million mammograms in Germany, a deep convolutional neural network (CNN) assigned a confidence score to each mammogram (Leibig et al., 2022). Assessments that the algorithm made with high confidence underwent no further workup, while low-confidence assessments were referred to the radiologist. This approach was associated with a 4 % increase in sensitivity and a 0.5 % increase in specificity compared with the assessment of a single radiologist unaided by the algorithm. In this scenario, 63 % of mammograms were automatically triaged by the algorithm, and the improved performance compared to a single radiologist’s reading was consistent across eight screening sites and three device manufacturers.

Lung cancer

Lung cancer is the leading cause of cancer deaths worldwide, causing almost 1.8 million deaths in 2020 (Sung et al., 2021). An example of a targeted screening approach, lung cancer screening is recommended based on individual risk. Screening of adults aged 50 to 80 years with a 20-pack-year smoking history using low-dose computed tomography (LDCT) has been recommended in the US since 2013 after initial studies showed a relative reduction in lung cancer mortality of 20% (Lung Cancer: Screening, 2021; National Lung Screening Trial Research Team et al., 2011). A similar screening program is being rolled out in the United Kingdom (NHS England, 2022).

lung cancer ebook

The study found that the use of the algorithm was associated with improved sensitivity to nodules across different levels of experience of the first reader.

In patients who undergo lung cancer screening using LDCT, AI has shown promise for the automatic detection of lung nodules likely to represent malignancy. This is important because the detection of lung nodules by radiologists is burdensome, time-consuming, and prone to error (Al Mohammad et al., 2019; Armato et al., 2009; Gierada et al., 2017; Leader et al., 2005). In a study of almost two thousand patients, a CNN-based algorithm designed to automatically detect lung nodules was tested as a second reader (Katase et al., 2022). The ground truth consisted of nodules identified by two experienced radiologists as being high-risk according to the clinical history as well as the nodule’s morphology. The study found that the use of the algorithm was associated with improved sensitivity to nodules across different levels of experience of the first reader. Although overall sensitivity was lower for ground glass nodules and nodules less than 1 cm in diameter, sensitivity for these was much higher when the algorithm was used compared to when the radiologist interpreted the images alone. False positives included areas of pleural inflammation or peripheral vessels while false negatives were often faint or poorly demarcated ground glass nodules or nodules close to the diaphragm. Importantly, the authors found consistent model performance across a range of CT radiation doses in a phantom study, indicating that their results might be generalizable to other chest CT protocols (Katase et al., 2022). Another study found a sensitivity of 93 % and specificity of 96 % of a CNNbased algorithm for the detection of lung nodules on LDCT compared to the consensus of two radiologists (Chamberlin et al., 2021). False positives in this study included areas of atelectasis, parenchymal changes associated with infection, and osteophytes protruding into the lung fields from thoracic vertebrae.

Beyond the mere identification of lung nodules, some studies have attempted to classify the risk of malignancy of identified nodules. A multi-component algorithm that includes lung segmentation, cancer region detection, and cancer prediction models was tested on 6716 LDCTs and validated on an independent dataset of 1139 LDCTs (Ardila et al., 2019). The algorithm outputs a probability of malignancy based on either single LDCTs or, when available, prior LDCTs from the same patient. Using a ground truth of biopsy-proven lung cancer, the algorithm performed as well as six radiologists when prior LDCTs were available. In the cases without prior LDCTs, the algorithm had an 11 % lower false positive rate and a 5 % lower false negative rate than the radiologists.

An assessment of the lung parenchyma on LDCT beyond the presence of lung nodules is a recent and promising approach to identifying the future risk of lung cancer. One study found that a 3D-CNN algorithm, tested on over 15,000 LDCTs, had an area under the receiver operating characteristic curve (AUC) of 0.86–0.94 (depending on the dataset) for predicting one-year lung cancer (Mikhael et al., 2023). Interestingly, the AUC of the algorithm after excluding cases where visible nodules were present at baseline in the same location as the future cancers was 0.82. The algorithm also showed a lower false positive rate than established scores of malignancy based on nodule morphology when the entire LDCT volume was assessed by the algorithm. These findings suggest that other features beyond the suspicious nodules were contributing to the algorithm’s prediction. Importantly, this means that the algorithm is detecting features in LDCT beyond what radiologists typically consider relevant for predicting lung cancer risk.

The eligibility criteria for lung cancer screening in the US, which come from the Centers for Medicare and Medicaid Services (CMS), miss over half of lung cancer cases (Y. Wang et al., 2015). Although other, more complex, score-based “pre-screening” tools exist, the information they require, such as the number of pack years, is often inaccurate or unavailable (Kinsinger et al., 2017). AI has thus been used to identify more individuals at high risk for lung cancer to include them in screening programs. A study of 5615 individuals found that a combination of plain chest radiographs, age, sex, and current smoking status allows a more targeted selection of patients for screening with LDCT (Lu et al., 2020). The model in this study had an AUC of 0.7 for predicting 12-year incident lung cancer compared to an AUC of 0.63 for the CMS criteria, translating to 30.7 % fewer lung cancer cases being missed with the algorithm. The model also predicted 12-year lung cancer mortality with an AUC of 0.76. The authors do not recommend routine chest radiographs for pre-screening but advocate the use of this model in patients undergoing chest radiographs for other clinical indications.

Colorectal cancer

Colorectal cancer is the third most common cancer in both women and men and is a major cause of cancer death worldwide (Sung et al., 2021). It develops as a cascade of events as intestinal mucosal cells accumulate genetic mutations, transforming first into hyperproliferative mucosa, then a benign adenoma, and, in some cases, an adenocarcinoma (Kuipers et al., 2015). Colorectal cancer screening is primarily preventive - it aims to detect potentially cancerous adenomas so that they can be removed, an approach that reduces the disease’s mortality (Zauber et al., 2012).

Colorectal cancer ebook

A recent proof-of-concept study used a fully automated approach using CNNs for polyp segmentation and distinguishing between benign and premalignant polyps.

Colorectal cancer screening is routinely done by either looking for blood in the stool using highly sensitive assays or by visualizing the lumen of the intestine using optical colonoscopy (Helsingen Lise M. & Kalager Mette, 2022). Optical colonoscopy is an established and reliable method for identifying colorectal adenomas and allows them to be immediately removed. However, its main disadvantages are low patient compliance and the need for sedation (Inadomi et al., 2012; Joseph et al., 2012; OECD, 2012; Stock et al., 2011; Use of Colorectal Cancer Screening Tests, 2023).

A promising emerging alternative to optical colonoscopy is computed tomography colonography. This technique has similar diagnostic accuracy to optical colonoscopy (Pickhardt et al., 2003, 2011, 2018), is preferred by patients (Ristvedt et al., 2003), and has better compliance (Moawad et al., 2010). It also does not require sedation and can pick up clinically relevant findings outside the bowel that are invisible to optical colonoscopy (Smyth et al., 2013). On the other hand, CT colonography requires bowel preparation (like optical colonoscopy), exposes the patient to some ionizing radiation, and does not allow for simultaneous polyp resection. Despite these disadvantages, the American College of Radiology recommends CT colonography for screening patients with average or moderate risk of colorectal cancer (Expert Panel on Gastrointestinal Imaging: et al., 2018).

CT colonography images undergo a series of preparation steps before being interpreted. These include preprocessing to remove artifacts, extracting the colon from the rest of the abdominal structures, 3D reconstruction of the colon, and visualization of the colon lumen. A recent study combined a novel colon segmentation and reconstruction method with polyp detection using a CNN (Alkabbany et al., 2022). The automated colon segmentation showed a more than 90 % overlap with manual expert segmentation in 70% of cases and colon polyps were detected with an AUC of 0.93, a sensitivity of 97 %, and a specificity of 79 %.

Differentiating between benign polyps and those with malignant potential is a challenge in both optical colonoscopy and CT colonography and has been the focus of several studies using AI. Radiomics-based approaches for classifying benign versus premalignant polyps on CT colonography have shown AUCs of up to 0.91 but require manual segmentation of the polyps (Grosu et al., 2021; Song et al., 2014). A recent proof-of-concept study used a fully automated approach using CNNs for polyp segmentation and distinguishing between benign and premalignant polyps (Wesp et al., 2022). The authors trained the CNN on data from 63 patients and tested it on an independent dataset of 59 patients, showing an AUC of up to 0.83 and a sensitivity and specificity of up to 80 % and 69 % respectively. Such AI-based approaches can potentially be used as a second reader to help guide the decision on polyp removal.

Hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer deaths in the world (Sung et al., 2021). Individuals with liver cirrhosis or chronic hepatitis B or C virus infection are at high risk for developing HCC (Vogel et al., 2022). Screening these patients is associated with a reduction in mortality from HCC (Singal et al., 2022; Zhang et al., 2004). Screening is usually performed using abdominal ultrasound every six months (European Association for the Study of the Liver, 2018; Frenette et al., 2019; Marrero et al., 2018) with or without measuring alpha-fetoprotein levels in the blood (Colli et al., 2006; Tzartzeva et al., 2018). Suspicious lesions identified on ultrasound are further characterized using either CT, MRI, or both.

Hepatocellular cancer ebook

Deep learning techniques have also been extensively applied in liver imaging using B-mode ultrasound showing promising results for detecting and classifying focal liver lesions as benign or malignant.

The pathogenesis of HCC involves a complex interplay between liver nodules that exist in different stages of chronic liver injury. Regenerative nodules form in response to hepatocyte damage and are commonly seen in cirrhotic livers. Genetic mutations can accumulate over time within these regenerative nodules, converting them to dysplastic ones with a high risk of progressing to HCC as more mutations accumulate (Kudo, 2009). Differentiating between dysplastic and malignant nodules using imaging is challenging (Park et al., 2017). Moreover, the imaging features of HCC sometimes overlap with those of other liver lesions, including hemangiomas, simple liver cysts, and focal nodular hyperplasia (Heiken, 2007).

Using a radiomics approach combining perfusion information and texture analysis in contrast-enhanced ultrasound, a study of 72 patients found a balanced accuracy of 0.84 for distinguishing between benign and malignant liver lesions (Turco et al., 2022). Another study using contrast-enhanced ultrasound found a sensitivity of 94.8 % and specificity of 93.6 % for distinguishing between HCC and focal nodular hyperplasia using a support vector machine learning approach (Huang et al., 2020), with other studies finding similar results (Gatos et al., 2015; Kondo et al., 2017). In a multicenter study investigating the differentiation of 11 different types of focal liver lesions using contrast-enhanced ultrasound and histopathology as a reference, support vector machine learning (AUC = 0.883) outperformed an artificial neural network (AUC = 0.829) and both approaches outperformed an experienced radiologist (AUC = 0.702) (Ta et al., 2018).

Deep learning techniques have also been extensively applied in liver imaging using B-mode ultrasound. These studies have shown promising results for detecting (Brehar et al., 2020; Schmauch et al., 2019; Tiyarattanachai et al., 2022) and classifying focal liver lesions as benign or malignant (Schmauch et al., 2019) or classifying them into specific entities (Hassan et al., 2017; Virmani et al., 2014). Using a deep learning approach, one study found that combining information on patient demographics and laboratory results with B-mode ultrasound images improved the AUC for classifying liver lesions as benign versus malignant from 0.721 (using ultrasound alone) to 0.994 (Sato et al., 2022). Another study of 334 patients found that the detection rate of focal liver lesions on B-mode ultrasound using a CNN was higher for HCC than for other focal liver lesions and the CNN outperformed human experts (with an algorithm detection rate of 100 % compared to 39.1 % for non-radiologists and 69.6 % for radiologists) (Tiyarattanachai et al., 2022).

Prostate cancer

Prostate cancer is the most common cancer in men in Europe and the United States (Ferlay et al., 2018; Siegel et al., 2021) and is the third most common cancer in the world (Sung et al., 2021). In countries where programs exist, screening is usually based on measuring levels of serum prostate-specific antigen (PSA). Serum PSA has high sensitivity but low specificity for prostate cancer (Merriel et al., 2022). Screening based on PSA alone thus leads to many unnecessary biopsies, with up to 75 % of systematic prostate biopsies - those done without targeting a specific location within the prostate, instead taking multiple biopsies from different parts of the gland - being negative (Ahmed et al., 2017). In addition, PSA screening tends to detect lower-risk and slower-growing cancer that is considered clinically insignificant because it does not threaten patient survival (US Preventive Services Task Force et al., 2018; Welch & Albertsen, 2020). Screening based on serum PSA levels followed by a systematic biopsy is thus overall of questionable benefit. Instead, the ideal approach would detect cancer and simultaneously characterize its clinical significance.

prostate cancer ebook

A study using a random forest-based classifier to detect suspicious areas on multiparametric prostate MRI was associated with Shorter reading times and improved specificity.

Multiparametric MRI plays an increasingly important role in the workup of screened prostate cancer cases and includes diffusion-weighted and T2-weighted sequences, with or without a T1-weighted dynamic contrast-enhanced sequence (Walker et al., 2020). False positives and the detection of clinically insignificant prostate cancer can be reduced using MRI, which may help reduce overtreatment (Drost et al., 2019). Studies suggest that MRI before biopsy can reduce the number of unnecessary biopsies by a third (Elwenspoek et al., 2019), and this approach has been included in several guidelines on prostate cancer management (Leitlinienprogramm Onkologie: Prostatakarzinom, n.d., Overview | Prostate Cancer: Diagnosis and Management | Guidance | NICE, n.d.; Mottet et al., 2017). MRI can also help direct targeted biopsies in patients with negative systematic prostate biopsies (Hoeks et al., 2012; Hugosson et al., 2022; Penzkofer et al., 2015; Siddiqui et al., 2015; Sonn et al., 2014). In patients found to have very low- or low-risk prostate cancer, MRI can be useful to actively monitor the disease, an approach that is associated with good long-term outcomes (Klotz et al., 2015). Reading prostate MRIs is challenging, however, and even standardized reporting systems have a steep learning curve and diagnostic performance varies greatly between radiologists and institutions (Kohestani et al., 2019; Muller et al., 2015; Rosenkrantz et al., 2017; Smith et al., 2019; Westphalen et al., 2020).

Segmentation of the entire prostate gland allows the determination of the gland’s volume, which is used for calculating the PSA density (a metric that helps differentiate between benign prostatic hypertrophy and prostate cancer) and radiotherapy planning. Manual prostate segmentation by radiologists is, however, time-consuming and prone to errors (Garvey et al., 2014). Automated segmentation of the prostate gland using AI-based tools is feasible and accurate, and several commercial tools are currently available for this purpose (AI for Radiology, n.d.; Bardis et al., 2021; Belue & Turkbey, 2022; Sanford et al., 2020; Sunoqrot et al., 2022; Turkbey & Haider, 2022; Ushinsky et al., 2021; van Leeuwen et al., 2021; B. Wang et al., 2019).

AI-based approaches have also proven useful for the identification and segmentation of prostate cancer on multiparametric MRI. Algorithms generally classify lesions either into two classes (e.g. clinically significant versus clinically insignificant prostate cancer) or multiple classes using the PI-RADS score (Belue & Turkbey, 2022; Twilt et al., 2021). In a multi-reader, multi-center study, using a random forest-based classifier to detect suspicious areas on multiparametric prostate MRI was associated with shorter reading times (2.7 to 4.4 minutes with the algorithm versus 3.5 to 6.3 minutes without the algorithm depending on reader experience) and improved specificity (71.5 % versus 44.8 %) (Gaur et al., 2018).

Several studies using deep learning approaches have achieved AUCs of up to 0.89 for detecting prostate cancer on multiparametric MRI (Arif et al., 2020; Saha et al., 2021). A commercially available deep-learning-based algorithm improved radiologists’ detection of clinically significant prostate cancer (using the consensus of three experienced radiologists as a reference), increased interreader reliability, and reduced median reading time (Winkel et al., 2021). Similar to the situation in breast cancer, diagnostic accuracy is highest when AI-based tools and radiologists’ interpretations are considered together rather than relying on the assessment of one or the other (Cacciamani et al., 2023).

AI has also been used to classify prostate cancer aggressiveness. In an MRI-based radiomics study, a support vector machine classifier was used to segment areas of prostate cancer, followed by texture analysis and quantitative feature extraction (Giannini et al., 2021). In the same study, another support vector machine classifier used the extracted features to classify tumor aggressiveness using histopathological grading as a reference. Trained on 72 patients’ data, the study found an AUC of 0.81 in a validation dataset of 59 patients (positive predictive value = 81 %, negative predictive value = 71 %). In another study of 107 patients’ multiparametric prostate MRIs, radiologists’ PI-RADS classifications were combined with a likelihood score derived from a random forest classifier, and all suspicious regions identified in this way were biopsied (Litjens et al., 2015). Including the algorithm’s score was associated with a higher probability of detecting prostate cancer (AUC = 0.88 with and 0.81 without the algorithm) and of detecting more aggressive cancers (AUC = 0.87 with and 0.78 without the algorithm). In a study of 417 patients a CNN achieved an AUC of 0.81 for classifying clinically significant prostate cancer using multiparametric MRI with only a slightly lower sensitivity compared to highly experienced radiologists (Cao et al., 2019).

Like with many other applications of AI in radiology, the lack of interpretability of deep learning models of prostate MRI hampers and delays their implementation in clinical practice (Aristidou et al., 2022; Reddy et al., 2020; Reyes et al., 2020; Vayena et al., 2018). A study using a CNN on prostate MRI from 1224 patients and histopathology as a reference found an AUC of 0.89 for distinguishing clinically significant prostate cancer from other prostate changes (Hamm et al., 2023). In addition, they included a voxelwise heat map of areas suspicious of clinically significant prostate cancer and PI-RADS-inspired descriptive explanations of how the CNN came to its conclusion. The algorithm was associated with a reduction in reading time from 85 seconds to 47 seconds and an increase in reading confidence in nonexpert readers.


Medical imaging plays a central role in the screening pathways of several of the most common cancers. Reading screening examinations requires considerable skill and experience, and current demand far exceeds the supply of trained radiologists (AAMC Report Reinforces Mounting Physician Shortage, 2021, Clinical Radiology UK Workforce Census 2019 Report, 2019). The use of AI-based tools for cancer screening holds immense promise for mitigating these issues. The benefits of such approaches have included improved identification of individuals eligible for screening, better diagnostic accuracy, reduced reporting times, and improved radiologists’ confidence in their own diagnostic decisions. The most promising results have been found when AI-based systems and radiologists have made decisions on screening examinations together. Collaborative decision-making between AI-based tools and radiologists can thus pave the way for a transformative era in cancer screening.


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