Final Literature Review – 388

with No Comments

Artificial intelligence in radiology cancer detection: a literature review


Artificial Intelligence, Cancer Detection, Tumor Detection, Machine Learning, Convolutional Neural Networks, Radiology


In a world where Artificial Intelligence (AI) is helping us drive our cars and control our traffic, it is to no surprise that AI is also getting involved in our healthcare. Computers have been used for many decades to help healthcare workers with both imaging patient organs and reading those images [4]. Now with the power of AI, new aspects of computer technology are helping healthcare workers and patients. Research has shown that radiologists have better sensitivity in their cancer detection diagnostics if they’ve had the help of AI-powered software [11], and in some cases the AI was able to outperform the radiologists [12]. The even more complex concept of detecting if the cancer has spread to other locations has also been tackled by AI [6]. Arguably AI can help minimize or eliminate human error in such a sensitive field where a patient’s life depends on how an image is interpreted [12]. Modern AI methods are more than capable of learning and correcting what triggers a radiologists to decide whether there is a cancerous tumor, whether a tumor is benign or whether cancer has spread to other places in the patient’s body. On the other hand, some AI methods are reliant on data from radiologists for their learning, and, as a result, these AI methods cannot completely replace human radiologists. In addition, all AI software needs to be maintained and updated over time to keep the integrity of the AI model, given that an outdated AI model can lose quality over time [10]. This literature review discusses different methods of radiology imaging, along with how they are used and what specific fields of cancer detection AI can be helpful in. Finally it focuses on how Convolutional Neural Networks can be trained to imitate a radiologist in detecting and diagnosing cancer and the spread of cancer.


Oncology is the branch of medicine that focuses on diagnosing and treating cancer patients. After the proper images are acquired through Computed Tomography (CT), Positron Emission Tomogra- phy (PET), Magnetic Resonance Imaging (MRI) or any other means of radiology imaging, the images need to be preprocessed in a for- mat that the AI can accept as input. Artificial Intelligence has made itself a crucial part of the oncology world by assisting in three clin- ical steps: 1-Detection , 2-Characterization and 3-Monitoring of the given cancerous abnormality[1]. Figure 1 shows the com- plete steps of AI assistance in oncology from image creation until final diagnostic and monitoring of the patient’s response to the

treatment. Specific tumor detection where AI has been applied to be useful with oncology or could potentially be useful are: Breast, Lung, Brain and Central Nervous System (CNS), Gastric, Prostate, Lymph node spreads, etc [1] [6].

2.1 Detection

Abnormality detection is defined as the process in which an AI system searches for a Region of Interest (ROI) in which any abnor- mality can be present [1] [7]. It is in this step where AI is aiming to correct any oversensitivity or undersensitivity in a human radi- ologist, reducing the number of false-negative and false-positive diagnostics. Computers have been helping with this step in oncol- ogy for over two decades in what is described as Computer Aided Detection (CADe) [3]. However, with rapid advancements in AI’s capabilities and popularity, CADe and AI-CADe have become an inseparable part of radiology imaging.

2.2 Characterization

After the detection of the abnormality, the detected abnormality needs to be characterized. Characterization includes separating the tumor or the area in question from the non-cancerous surrounding tissue, classifying the tumor as malignant or benign and finally determining the stage of cancerous tumor based on how much the tumor has spread. These three steps in characterization are com- monly referred to as: Segmentation, Diagnosis and Staging[4] [7].

Ali Farahmand
Computer Science Department at Earlham College Richmond, Indiana


• Segmentation is a process similar to edge detection in im- age processing, as it aims to narrow down the image and only draw focus to the cancerous part of the image.

• Computer Aided Diagnosis (CADx) is the name of systems used in the diagnosis part of characterization [4]. CADx sys- tems use characteristic features such as texture and intensity to determine the malignancy of the tumor [4].

There are specific criteria that put each patient into pre- specified stages according to data acquired in the segmen- tation and diagnosis steps. Staging systems use measures such as the size of the tumor, whether the cancer has spread out of the tumor (Metastasizing) and the number of nearby lymph nodes where the cancer has spread to.


The final step where AI assists in oncology is monitoring a patient’s response after a period of time in which the patient underwent one or a series of treatments, such as chemotherapy. The AI is effectively looking for any changes in the tumor, such as growth or shrinking in size, changes in the tumor’s malignancy and the spread. The AI system can do this accurately since it can detect changes that

might not be visible to the radiologists’ eyes. The AI system also eliminates the human error involved in reading smaller changes in images and comparison between images over time [1].


Machine Learning is mostly data driven. Thus, AI systems have a constant need for patient radiology records in order to be of any assistance to radiology practices or to have the ability to compete with human radiologists. Fortunately, there is no lack of data or variety of data in this field as statistics show that one in four Amer- icans receive a CT scan and one in ten Americans receive an MRI scan each year [7].

Furthermore, industry famous dataset libraries are publicly avail- able, including but not limited to:

• OpenNeuro [9] (formerly known as OpenfMRI [8]) • Camelyon17 as part of the camelyon challenge [5] • BrainLife [2]


Different AI algorithms and methods have been used in oncology both for Computer-Aided Detection (CADe) and for Computer- Aided Diagnosis (CADx). Traditionally, supervised, labeled data and shallow networks have been used. However, with advancements in AI technology, unsupervised, unlabeled and deeper networks have proven to be of more help in detecting patterns for CADe and CADx systems. Deep learning methods might even find patterns that are not easily detectable to the human eye [15].

4.1 Support Vector Machines

Support Vector Machine (SVM) is a supervised machine learning model that is more on the traditional side of models for CAD sys- tems. Due to their simplicity and the fact that they aren’t very computationally expensive, SVMs have been used extensively in tumor detection and have yielded good results [1]. However they’ve been succeeded with more advanced machine learning methods that are capable of detecting features without having labeled data as their input.

4.2 Convolutional Neural Networks

Convolutional Neural Networks are commonly used for unsuper- vised learning and are considered to be deep learning meaning they include many more layers than supervised versions of neural

networks. CNNs are optimized for having images as input and since radiology is image focused, CNNs are one of the most common AI methods used in radiology [14]. In a 2016 study by Shin et al. on different CNN applications, CNNs yielded better average results than traditional approaches on lymph node datasets [13]. CNNs layers consists of convolution and pooling layers. Convolution lay- ers include filters which through training, learn to create a feature map which outputs detected features in the input [14]. Pooling layers are used for downsizing the output of the convolution lay- ers which helps with reducing the computation and overfitting issues [14]. What makes CNNs unique from other multi-layered machine learning models is feature extracting which is powered by the combination of convolution and pooling layers. At the end of the last convolution and pooling layers there is fully connected (FC) layer which is used as the classifier after the feature extracting process [14]. There are multiple architectures for CNNs which use different layer combinations [14] and these architectures are used in detection, segmentation and diagnosis steps of oncology [13].


This literature review looked into how AI has been used in radiology for detecting, diagnosing and monitoring cancer in patients. We discussed the main steps the AI applies to oncology, from when the image has been acquired by CT, PET or MRI scanner machines, to when the AI-CAD system reads the image to detect an ROI and diagnose the tumor, to how AI can be helpful to monitor the shrink or growth of tumors in a patient that is undergoing a treatment for the tumor. We further discussed how AI systems are capable of detecting metastasis which it categorizes the patient into different stages depending on how much the cancer has spread away from the initial tumor. After discussing the data and glancing over a few important datasets we looked at different AI, both supervised and unsupervised, and discussed how they differ.


  1. [1]  Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, Maryellen L. Giger, Nicolai J. Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christo- pher Abbosh, Ian F. Dunn, Raymond H. Mak, Rulla M. Tamimi, Clare M. Tem- pany, Charles Swanton, Udo Hoffmann, Lawrence H. Schwartz, Robert J. Gillies, Raymond Y. Huang, and Hugo J. W. L. Aerts. 2019. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Jour- nal for Clinicians 69, 2 (2019), 127–157. arXiv:
  2. [2] [n.d.]. Brain Life dataset library. Retrieved September 17, 2020 from
  3. [3]  RonaldACastellino.2005.Computeraideddetection(CAD):anoverview.Cancer Imaging 5, 1 (2005), 17.

Figure 1: The main AI steps in Oncology

Ali Farahmandpage2image49010784

Artificial intelligence in radiology cancer detection: a literature review

[4] Macedo Firmino, Giovani Angelo, Higor Morais, Marcel R Dantas, and Ricardo Valentim. 2016. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomedical engineering online 15, 1 (2016), 1–17.

[11] [12] [13] [14] [15]

Alejandro Rodríguez-Ruiz, Elizabeth Krupinski, Jan-Jurre Mordang, Kathy Schilling, Sylvia H Heywang-Köbrunner, Ioannis Sechopoulos, and Ritse M Mann. 2019. Detection of breast cancer with mammography: effect of an artificial in- telligence support system. Radiology 290, 2 (2019), 305–314. 1148/radiol.2018181371 arXiv: Alejandro Rodriguez-Ruiz, Kristina Lång, Albert Gubern-Merida, Mireille Broed- ers, Gisella Gennaro, Paola Clauser, Thomas H Helbich, Margarita Chevalier, Tao Tan, Thomas Mertelmeier, et al. 2019. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute 111, 9 (2019), 916–922.

H. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. 2016. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging 35, 5 (2016), 1285–1298.

Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, and Eyal Klang. 2019. Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide. Radiology 290, 3 (2019), 590–606. 10.1148/radiol.2018180547

An Tang, Roger Tam, Alexandre Cadrin-Chênevert, Will Guest, Jaron Chong, Joseph Barfett, Leonid Chepelev, Robyn Cairns, J Ross Mitchell, Mark D Cicero, et al. 2018. Canadian Association of Radiologists white paper on artificial intel- ligence in radiology. Canadian Association of Radiologists Journal 69, 2 (2018), 120–135.

[5] grand [n.d.]. Camelyon17 grand challenge. 17, 2020 from https://camelyon17.grand-

Retrieved September

  1. [6]  Richard Ha, Peter Chang, Jenika Karcich, Simukayi Mutasa, Reza Fardanesh, Ralph T Wynn, Michael Z Liu, and Sachin Jambawalikar. 2018. Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset. Jour- nal of Digital Imaging 31, 6 (2018), 851–856.
  2. [7]  Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts. 2018. Artificial intelligence in radiology. Nature Reviews Cancer 18, 8 (2018), 500–510.
  3. [8]  G. Manogaran, P. M. Shakeel, A. S. Hassanein, P. Malarvizhi Kumar, and G. Chandra Babu. 2019. Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection anData Sample Imbalance Analysis. IEEE Access 7 (2019), 12–19.
[9] [n.d.]. Open Neuro dataset library. 2020 from

Retrieved September 17,

[10] Oleg S Pianykh, Georg Langs, Marc Dewey, Dieter R Enzmann, Christian J Herold, Stefan O Schoenberg, and James A Brink. 2020. Continuous learning AI in radiology: implementation principles and early applications. Radiology (2020), 200038.

Leave a Reply