Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction
Pages 58 - 64
Abstract
Colorectal cancer (CRC) management, particularly for invasive histopathology analysis, has benefited from the prognostic insights the tumor-stroma ratio (TSR) provides. Despite advances in colonography, histopathology, and radiomics, the relationship between TSR and radiomic features still needs to be studied, given the potential non-invasive nature of radiomics biomarkers. This study explores the relationship between TSR and radiomics that can reduce the reliance on invasive biopsies in CRC management by identifying noninvasive radiomic-based markers. A retrospective analysis was conducted on radiology slides and corresponding histopathology slides from the colorectal cancer patients (n = 15) sourced from The Cancer Genomic Atlas (TCGA). A specialist radiologist manually segmented the tumors, and radiomic feature extraction was performed on radiology slides. A pre-trained vision transformer model characterized pathology slides. Using a tile-based approach, each tile was classified into one of nine distinct classes, resulting in a complete characterization of the entire slide. TSR was estimated as the stroma ratio to tumor area on pathology slides. Statistical correlation analysis and machine learning algorithms were employed to find associations between TSR and radiomic features, including feature importance assessment. Our findings indicate a significant correlation between specific gray-level measurement features and TSR, suggesting the potential of these radiomic features as noninvasive prognostic indicators. In summary, the interaction between radiomics and tumor-stroma ratio (TSR) presents potential opportunities for improving colorectal cancer (CRC) prognosis, treatment decision-making, and patient stratification.
References
[1]
Bray F, Ferlay J, Soerjomataran I, Siegel RL, Torre LA, Jemal A. 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. Vol 68, pp. 394-424 (November 2018). https://doi.org/10.3322/caac.21492
[2]
Sifaki-Pistolla D, Poimenaki V, Fotopoulou I, Saloustros E, Mavroudis D, Vamvaks L, Lionis C. 2022. Significant Rise of Colorectal Cancer Incidence in Younger Adults and Strong Determinants: 30 Years Longitudinal Differences between under and over 50s. Cancers. Vol. 14, pp. 4799. (September 2022). https://doi.org/10.3390/cancers14194799
[3]
Nishihara R, Wu K, Lochhead P, Morikawa T, Liao X, Qian ZR, Inamura K, Kim SA, Kuchiba A, Yamauchi M, Imamura Y, Willet WC, Rosner BA, Fuchs CS, Giovannucci E, Ogino S, Chan AT. 2013. Long-term colorectal-cancer incidence and mortality after lower endoscopy. N. Engl. J. Med. Vol. 369, pp. 1095–1105. (September 2013). https://doi.org/10.1056/NEJMoa1301969
[4]
Quintero E. Et al. 2012. Colonoscopy versus Fecal Immunochemical Testing in Colorectal-Cancer Screening. N. Engl. J. Med. Vol 366, pp. 697-706. (February 2012). https://doi.org/10.1056/NEJMoa1108895
[5]
Fuchs TJ, and Buhmann JM. 2011. Computational pathology: Challenges and promises for tissue analysis. Comput. Med. Imaging Graph. Vol. 35, pp. 515–530. (October 2011). https://doi.org/10.1016/j.compmedimag.2011.02.006
[6]
Zhou C, Jin Y, Chen Y, Huang S, Rengpeng H, Wang Y, Zhao Y, Chen Y, Guo L, Liao J. 2021. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Comput. Med. Imaging Graph. Vol. 88, pp. 101861. (March 2021). https://doi.org/10.1016/j.compmedimag.2021.101861
[7]
Jiao Y, Li J, Qian C, Fei S. 2021. Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images. Comput. Methods Programs Biomed. Vol. 204, pp. 106047. https://doi.org/10.1016/j.cmpb.2021.106047
[8]
Kather JN. 2019. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLOS Med. Vol. 16, pp. e1002730. (January 2019). https://doi.org/10.1371/journal.pmed.1002730
[9]
Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, Wu L, Huang Y, Liang C, Liu Z. 2020. Artificial intelligence quantified tumor-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. Vol. 61, pp. 103054. (November 2020). https://doi.org/10.1016/j.ebiom.2020.103054
[10]
Smit MA, Ciompi F, Bokhorst J-M, van Pelt GW, Geessink OGF, Putter H, Tollenaar RAEM, van Krieken JHJM, Mesker WE, van der Laak JAWM. 2023. Deep learning-based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment. J. Pathol. Inform. Vol. 14, pp. 100191. (January 2023). https://doi.org/10.1016/j.jpi.2023.100191
[11]
Obaro AE, Burling DN, Plumb AA. 2018. Colon cancer screening with CT colonography: logistics, cost-effectiveness, efficiency and progress. Br. J. Radiol. Vol. 91, pp. 20180307. (October 2018). https://doi.org/10.1259/bjr.20180307
[12]
Lambin P 2012. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer, Vol. 48, pp. 441–446. (March 2012). https://doi.org/10.1016/j.ejca.2011.11.036
[13]
Gillies RJ, Kinahan PE, Hricak H. 2016. Radiomics: Images Are More than Pictures, They Are Data. Radiology. Vol. 278, pp. 563–577. (February 2016). https://doi.org/10.1148/radiol.2015151169
[14]
Kumar V Radiomics: the process and the challenges. Magn. Reson. Imaging. Vol. 30, pp. 1234–1248. (November 2012). https://doi.org/10.1016/j.mri.2012.06.010
[15]
Ganeshan B, Miles KA. 2013. Quantifying tumour heterogeneity with CT. Cancer Imaging. Vol. 13, pp. 140–149. (March 2013). https://doi.org/10.1102/1470-7330.2013.0015
[16]
Cui 2023. Model integrating CT-based radiomics and genomics for survival prediction in esophageal cancer patients receiving definitive chemoradiotheraphy. Biomark. Res. Vol 11, pp. 44. (April 2023). https://doi.org/10.1186/s40364-023-00480-x
[17]
Wang 2020. A radiomics-based model on non-contrast CT predicting cirrhosis: make the most of image data. Biomark. Res. Vol 8, pp. 47. (September 2020). https://doi.org/10.1186/s40364-020-00219-y
[18]
The Cancer Genome Atlas Network. 2012. Comprehensive molecular characterization of human colon and rectal cancer. Nature. Vol. 487, pp. 330–337. (Jul 2012). https://doi.org/10.1038/nature11252
[19]
Liu L 2018. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. Vol. 173, pp. 400-416.e11. (April 2018). https://doi.org/10.1016/j.cell.2018.02.052
[20]
Yushkevich PA, Gao Y, and Gerig G. 2016. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In Proceedings from 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA: IEEE. pp. 3342–3345. (August 2016). https://doi./10.1109/EMBC.2016.7591443
[21]
van Griethuysen JJM 2017. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. Vol. 77, pp. e104–e107. (November 2017). https://doi.org/10.1158/0008-5472.CAN-17-0339
[22]
Zwanenburg A 2020. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. Vol. 295, pp. 328–338. (May 2020). https://doi.org/10.1148/radiol.2020191145
[23]
Haralick RM, Shanmugam K and Dinstein I. 1973. Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern. Vol. SMC-3, pp. 610–621. (November 1973). https://doi.org/10.1109/TSMC.1973.4309314
[24]
Li J, Chen J, Tang Y, Wang C, Landman BA, Zhou SK. 2023. Transforming medical imaging with Transformers? A comparative review of key properties, current progress, and future perspectives. Med. Image Anal. Vol. 85, pp. 102762. (Apr 2023). https://doi.org/10.1016/j.media.2023.102762
[25]
Dosovitskiy A 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In Proceedings from the 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria. (May 3-7, 2021) [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
[26]
Wang X, Yang S, Zhang J, Wang M, Zhang J, Yang W, Huang J, Han X. 2022. TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification. Med. Image Anal. Vol 81, pp 102559. (October 2022). https://doi.org/10.1016/j.media.2022.102559
[27]
Yip SSF and Aerts HJWL. 2016. Applications and limitations of radiomics. Phys. Med. Biol. Vol. 61, pp. R150–R166. (Jul 2016). https://doi.org/10.1088/0031-9155/61/13/R150
[28]
Coroller TP 2015. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother. Oncol. Vol. 114, pp. 345–350. (March 2015). https://doi.org/10.1016/j.radonc.2015.02.015
[29]
Henriksson E, Kjellen E, Wahlberg P, Ohlsson T, Wennerberg J, Brun E. 2007. 2-Deoxy-2-[18F] fluoro-D-Glucose Uptake and Correlation to Intratumoral Heterogeneity. Anticancer Res. Vol 27(4B), pp. 2155. (July 2007)
[30]
Yang X and Knopp MV. 2011. Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review. J. Biomed. Biotechnol. pp. 732848. (April 2011). https://doi.org/10.1155/2011/732848
[31]
Jiang 2021. Detection and clinical significance of circulating tumor cells in colorectal cancer. Biomark. Res. Vol 9, pp. 85. (November 2021). https://doi.org/10.1186/s40364-021-00326-4
[32]
Sullivan L, Pacheco RR, Kmeid M, Chen A, Lee H. 2022. Tumor Stroma Ratio and Its Significance in Locally Advanced Colorectal Cancer. Curr. Oncol. Vol. 29, pp. 3232–3241. (May 2022). https://doi.org/10.3390/curroncol29050263
[33]
Gao J, Shen Z, Deng Z, Mei L. 2021. Impact of Tumor–Stroma Ratio on the Prognosis of Colorectal Cancer: A Systematic Review. Front. Oncol. Vol. 11, pp. 738080. (Nov 2021). https://doi.org/10.3389/fonc.2021.738080
[34]
Vaupel P.; Mayer A. 2007. Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev. Vol. 26, pp. 225–239. (June 2007). https://doi.org/10.1007/s10555-007-9055-1
[35]
Nordsmar M, Overgaard J. 2004. Tumor hypoxia is independent of hemoglobin and prognostic for loco-regional tumor control after primary radiotherapy in advanced head and neck cancer. Acta Ocol. Vol 43, pp. 396-403. https://doi.org/10.1080/02841860410026189
[36]
Sun 2018. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. Vol 19, pp 1180-1191. (September 2018). https://doi.org/10.1016/S1470-2045(18)30413-3
[37]
Wang X, Xie T, Luo J, Zhou Z, Yu X, Guo X. 2022. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res. Vol. 24, pp. 20. (December 2022). https://doi.org/10.1186/s13058-022-01516-0
[38]
Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. 2022. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. Vol. 19, pp. 132–146. (February 2022). https://doi.org/10.1038/s41571-021-00560-7
[39]
Wang R Et al. 2022. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J. Hematol. Oncol. Vol. 15, pp. 11. (January 2022). https://doi.org/10.1186/s13045-022-01225-3
Index Terms
- Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction
Recommendations
Classification of colorectal cancer in histological images using deep neural networks: an investigation
AbstractColorectal cancer refers to cancer of the colon or rectum; and has high incidence rates worldwide. Colorectal cancer most often occurs in the form of adenocarcinoma, which is known to arise from adenoma, a precancerous lesion. In general, ...
Discrimination between tumour epithelium and stroma via perception-based features
In this work we propose the use of image features based on visual perception for discriminating epithelium and stroma in histological images. In particular, we assess the capability of the following five visual features to correctly discriminate ...
Image Processing Based Colorectal Cancer Detection in Histopathological Images
The article proposes an image processing-based automatic methodology for early diagnosis of colorectal cancer. In pathology, staining and sectioning of tissues are routinely used as a primary technique to detect cancer. In this methodology, the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
349 pages
ISBN:9798400716874
DOI:10.1145/3673971
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 September 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Sichuan Natural Science Foundation
- National Natural Science Foundation
- University of Electronic Science and Technology of China
Conference
ICMHI 2024
ICMHI 2024: 2024 8th International Conference on Medical and Health Informatics
May 17 - 19, 2024
Yokohama, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 13Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)4
Reflects downloads up to 19 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format