1,721,304 research outputs found
Supplementary_table_1 – Supplemental material for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay
No full textSupplemental material, Supplementary_table_1 for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay by Kajsa Björkman, Harri Mustonen, Tuomas Kaprio, Caj Haglund and Camilla Böckelman in Tumor Biology</p
Supplementary_Table_2 – Supplemental material for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay
No full textSupplemental material, Supplementary_Table_2 for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay by Kajsa Björkman, Harri Mustonen, Tuomas Kaprio, Caj Haglund and Camilla Böckelman in Tumor Biology</p
Supplementary_table_3 – Supplemental material for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay
No full textSupplemental material, Supplementary_table_3 for Mucin 16 and kallikrein 13 as potential prognostic factors in colon cancer: Results of an oncological 92-multiplex immunoassay by Kajsa Björkman, Harri Mustonen, Tuomas Kaprio, Caj Haglund and Camilla Böckelman in Tumor Biology</p
Supplementary_Table – Supplemental material for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study
No full textSupplemental material, Supplementary_Table for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study by Sebastian Stenman, Päivi Siironen, Harri Mustonen, Johan Lundin, Caj Haglund and Johanna Arola in Tumor Biology</p
SupFigure – Supplemental material for Serum MMP-8 and TIMP-1 as prognostic biomarkers in gastric cancer
No full textSupplemental material, SupFigure for Serum MMP-8 and TIMP-1 as prognostic biomarkers in gastric cancer by Alli Laitinen, Jaana Hagström, Harri Mustonen, Arto Kokkola, Taina Tervahartiala, Timo Sorsa, Camilla Böckelman and Caj Haglund in Tumor Biology</p
sj-docx-2-sjs-10.1177_14574969221076792 – Supplemental material for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance
No full textSupplemental material, sj-docx-2-sjs-10.1177_14574969221076792 for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance by Katarina Johansson, Tuomas Kaprio, Heini Nieminen, Tiina E. Lehtimäki, Eila Lantto, Caj Haglund and Hanna Seppänen in Scandinavian Journal of Surgery</p
sj-docx-3-sjs-10.1177_14574969221076792 – Supplemental material for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance
No full textSupplemental material, sj-docx-3-sjs-10.1177_14574969221076792 for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance by Katarina Johansson, Tuomas Kaprio, Heini Nieminen, Tiina E. Lehtimäki, Eila Lantto, Caj Haglund and Hanna Seppänen in Scandinavian Journal of Surgery</p
sj-docx-1-sjs-10.1177_14574969221076792 – Supplemental material for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance
No full textSupplemental material, sj-docx-1-sjs-10.1177_14574969221076792 for A retrospective study of intraductal papillary neoplasia of the pancreas (IPMN) under surveillance by Katarina Johansson, Tuomas Kaprio, Heini Nieminen, Tiina E. Lehtimäki, Eila Lantto, Caj Haglund and Hanna Seppänen in Scandinavian Journal of Surgery</p
Abstract 5718: Outcome prediction in colorectal cancer using digitized tumor samples and machine learning
No full textAbstract
Additional prognostic stratification of colorectal cancer patients is needed to improve management of patients. Visual microscopic assessment of tumor samples remains the standard method for disease subtyping. However, visual analysis of samples is subjective and weakly reproducible due to inter- and intra-observer variations. Recent progress within machine learning, especially its novel branch called deep learning, enables accurate evaluation of complex patterns observed in microscopic tissue images. Here, we combined deep learning techniques to evaluate a set of digitized formalin fixed paraffin embedded hematoxylin-eosin stained tumor tissue microarray (TMA) samples from 420 randomly selected patients with colorectal cancer. For each patient a set of clinicopathological characteristics including histological grade, Dukes stage and age at diagnosis are available as well as outcome data. Using convolutional neural networks and Long Short-Term Memory networks we validated the predictive power of the colorectal TMAs with regards to patient outcome. Univariate Cox proportional hazard regression analysis demonstrated that the prognostic accuracy of the deep learning algorithm on TMAs (hazard ratio 2.3; CI 95% 1.79-3.03) outperforms visual histological grading performed by a certified pathologist on a whole slide level (hazard ratio 1.65; CI 95% 1.30-2.15). In multivariate Cox proportional hazard regression, the deep learning based model was a prognostic factor, independent of histological grade, Dukes stage and age at diagnosis (Wald p-value &lt; 0.001). Thus, we demonstrate that novel deep learning models can serve as digital prognostic biomarkers in colorectal cancer.
Citation Format: Dmitrii Bychkov, Riku Turkki, Caj Haglund, Nina Linder, Johan Lundin. Outcome prediction in colorectal cancer using digitized tumor samples and machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5718. doi:10.1158/1538-7445.AM2017-5718</jats:p
- …
