Heuristic Oncological Prognosis Evaluator (HOPE): Deep-Learning Framework to Detect Multiple Cancers





Artificial intelligence, Transfer learning, Cancer diagnosis, Pathomics, Radiomics


Cancer is the common name used to categorize a collection of diseases. In the United States, there were an estimated 1.8 million new cancer cases and 600,000 cancer deaths in 2020. Though it has been proven that an early diagnosis can significantly reduce cancer mortality, cancer screening is inaccessible to much of the world’s population. Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. A literature search with the Google Scholar and PubMed databases from January 2020 to June 2021 determined that currently, no machine learning model (n=0/417) has an accuracy of 90% or higher in diagnosing multiple cancers. We propose our model HOPE, the Heuristic Oncological Prognosis Evaluator, a transfer learning diagnostic tool for the screening of patients with common cancers. By applying this approach to magnetic resonance (MRI) and digital whole slide pathology images, HOPE 2.0 demonstrates an overall accuracy of 95.52% in classifying brain, breast, colorectal, and lung cancer. HOPE 2.0 is a unique state-of-the-art model, as it possesses the ability to analyze multiple types of image data (radiology and pathology) and has an accuracy higher than existing models. HOPE 2.0 may ultimately aid in accelerating the diagnosis of multiple cancer types, resulting in improved clinical outcomes compared to previous research that focused on singular cancer diagnosis.


Download data is not yet available.

Author Biographies

Lee Conrad, Mentor, Little Rock Central High School

Department of Chemistry at Little Rock Central High School

Fred Prior, Mentor, University of Arkansas Medical Sciences

Professor and Chair of the Department of Biomedical Informatics

Professor of Radiology at the University of Arkansas for Medical Sciences

References or Bibliography

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., & Pringle, M. (2013). The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging, 26(6), 1045-1057. https://doi.org/10.1007/s10278-013-9622-7

CPTAC. (2018). Radiology Data From the Clinical Proteomic Tumor Analysis Consortium Lung Squamous Cell Carcinoma [Cptac-Lscc] Collection [Data Set]. https://doi.org/https://doi.org/10.7937/k9/tcia.2018.3rje41q1

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., Petridis, P. D., Marie, T., Shah, A. H., Garton, H. J. L., Maher, C. O., Heth, J. A., McKean, E. L., Sullivan, S. E., Hervey-Jumper, S. L., Patil, P. G., Thompson, B. G., Sagher, O., McKhann, G. M., Komotar, R. J., Ivan, M. E., Snuderl, M., Otten, M. L., Johnson, T. D., Sisti, M. B., Bruce, J. N., Muraszko, K. M., Trautman, J., Freudiger, C. W., Canoll, P., Lee, H., Camelo-Piragua, S., & Orringer, D. A. (2020). Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine, 26(1), 52-58. https://doi.org/10.1038/s41591-019-0715-9

Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. Sage.

Kaggle. (2019). BreakHis-v1. https://www.kaggle.com/rangan2510/breakhisv1

Kaggle. (2020a). Brain Tumor Segmentation. https://www.kaggle.com/leaderandpiller/brain-tumor-segmentation

Kaggle. (2020b). Lung and Colon Cancer Histopathological Images. https://www.kaggle.com/andrewmvd/lung-and-colon-cancer-histopathological-images

Kumar, R., Gupta, A., Arora, H. S., Pandian, G. N., & Raman, B. (2020). CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features. IEEE Access, 8, 79440-79458. https://doi.org/10.1109/ACCESS.2020.2989193

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Li, X., Hu, B., Li, H., & You, B. (2019). Application of artificial intelligence in the diagnosis of multiple primary lung cancer. Thoracic cancer, 10(11), 2168-2174. https://doi.org/ https://doi.org/10.1111/1759-7714.13185

Li, Y., Eresen, A., Shangguan, J., Yang, J., Lu, Y., Chen, D., Wang, J., Velichko, Y., Yaghmai, V., & Zhang, Z. (2019). Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer. Am J Cancer Res, 9(11), 2482-2492.

McCarthy, J. (2007). What is artificial intelligence? Retrieved 9/11/21 from

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine, 6(7), e1000097. https://doi.org/


Nartowt, B. J., Hart, G. R., Roffman, D. A., Llor, X., Ali, I., Muhammad, W., Liang, Y., & Deng, J. (2019). Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data. PloS one, 14(8), e0221421. https://doi.org/10.1371/journal.pone.0221421

Prior, F., Almeida, J., Kathiravelu, P., Kurc, T., Smith, K., Fitzgerald, T. J., & Saltz, J. (2020). Open access image repositories: high-quality data to enable machine learning research. Clinical Radiology, 75(1), 7-12. https://doi.org/https://doi.org/10.1016/j.crad.2019.04.002

Raghu, M., Zhang, C., Kleinberg, J., & Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. arXiv preprint arXiv:1902.07208.

Sadoughi, F., Kazemy, Z., Hamedan, F., Owji, L., Rahmanikatigari, M., & Azadboni, T. T. (2018). Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer (Dove Med Press), 10, 219-230. https://doi.org/10.2147/bctt.S175311

Saltz, J., Gupta, R., & Hou, L. (2018). Tumor-infiltrating lymphocytes maps from tcga h&e whole slide pathology images. https://doi.org/https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1

Sathya, R., & Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence, 2(2), 34-38.

Scarpace, L., Mikkelsen, L., Cha, T., Rao, S., Tekchandani, S., Gutman, S., & Pierce, D. (2016). Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection. https://doi.org/https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9

Sepandi, M., Taghdir, M., Rezaianzadeh, A., & Rahimikazerooni, S. (2018). Assessing Breast Cancer Risk with an Artificial Neural Network. Asian Pac J Cancer Prev, 19(4), 1017-1019. https://doi.org/10.22034/apjcp.2018.19.4.1017

Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3), 209-249. https://doi.org/ https://doi.org/10.3322/caac.21660

Torrey, L., & Shavlik, J. (2010). Transfer learning. In Handbook of research on machine learning applications and trends: algorithms, methods, and techniques (pp. 242-264). IGI global.

Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 49, 433-460. https://www.csee.umbc.edu/courses/471/papers/turing.pdf

Zeng, Y., & Zhang, J. (2020). A machine learning model for detecting invasive ductal carcinoma with Google Cloud AutoML Vision. Computers in Biology and Medicine, 122, 103861. https://doi.org/https://doi.org/10.1016/j.compbiomed.2020.103861

Zhao, L., Bai, C. X., & Zhu, Y. (2020). Diagnostic value of artificial intelligence in early-stage lung cancer. Chin Med J (Engl), 133(4), 503-504. https://doi.org/10.1097/cm9.0000000000000634



How to Cite

Iyer, A., Conrad, L., & Prior, F. (2021). Heuristic Oncological Prognosis Evaluator (HOPE): Deep-Learning Framework to Detect Multiple Cancers. Journal of Student Research, 10(3). https://doi.org/10.47611/jsrhs.v10i3.2070



HS Research Articles