Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer

[1]  C. Waltz.  Validation study. , 1988, NLN publications.

[2]  Nico Karssemeijer,et al.  Stain Specific Standardization of Whole-Slide Histopathological Images , 2016, IEEE Transactions on Medical Imaging.

[3]  D C McMillan,et al.  The relationship between the tumour stroma percentage, clinicopathological characteristics and outcome in patients with operable ductal breast cancer , 2014, British Journal of Cancer.

[4]  Tiehong Zhang,et al.  Tumor-stroma ratio is an independent predictor for survival in NSCLC. , 2015, International journal of clinical and experimental pathology.

[5]  A. Jaffe,et al.  Reduction of infarct size in patients with inferior infarction with intravenous glyceryl trinitrate. A randomised study. , 1983, British heart journal.

[6]  R. Tollenaar,et al.  The tumour–stroma ratio in colon cancer: the biological role and its prognostic impact , 2018, Histopathology.

[7]  B F Warren,et al.  The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: validation in the VICTOR trial. , 2013, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  B. van Ginneken,et al.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.

[9]  D. Carter.  TNM Classification of Malignant Tumors , 1998 .

[10]  P. V. van Diest,et al.  Tumor-stroma ratio as prognostic factor for survival in rectal adenocarcinoma: A retrospective cohort study , 2017, World journal of gastrointestinal oncology.

[11]  Francesco Bianconi,et al.  Discrimination between tumour epithelium and stroma via perception-based features , 2015, Neurocomputing.

[12]  John E. Tomaszewski,et al.  Digital Pathology , 2020, Lecture Notes in Computer Science.

[13]  R. Labianca,et al.  ESMO Consensus Guidelines for management of patients with colon and rectal cancer. a personalized approach to clinical decision making. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.

[14]  H. Putter,et al.  Uniform Noting for International Application of the Tumor-Stroma Ratio as an Easy Diagnostic Tool: Protocol for a Multicenter Prospective Cohort Study , 2019, JMIR research protocols.

[15]  Bram van Ginneken,et al.  The importance of stain normalization in colorectal tissue classification with convolutional networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[16]  Derek R. Magee,et al.  Towards automatic patient selection for chemotherapy in colorectal cancer trials , 2014, Medical Imaging.

[17]  Hans J. Tanke,et al.  The Carcinoma–Stromal Ratio of Colon Carcinoma Is an Independent Factor for Survival Compared to Lymph Node Status and Tumor Stage , 2007, Cellular oncology : the official journal of the International Society for Cellular Oncology.

[18]  H. Grabsch,et al.  The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients , 2010, British Journal of Cancer.

[19]  R. Vink,et al.  The prognostic value of tumour-stroma ratio in triple-negative breast cancer. , 2012, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[20]  Cornelis H. Slump,et al.  Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of H and E stained rectal carcinomas , 2015, Medical Imaging.

[21]  F. B. Sørensen,et al.  Stroma-High Lymph Node Involvement Predicts Poor Survival More Accurately for Patients with Stage III Colon Cancer , 2016 .

[22]  L. Wyrwicz,et al.  Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[23]  Yong Song,et al.  肿瘤间质比是非小细胞肺癌独立的预后因素 , 2013, Zhongguo fei ai za zhi = Chinese journal of lung cancer.

[24]  L. Påhlman,et al.  Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[25]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[26]  R. Tollenaar,et al.  The prognostic value of the tumour-stroma ratio in primary operable invasive cancer of the breast: a validation study , 2017, Breast Cancer Research and Treatment.

[27]  Dennis Andersson,et al.  A retrospective cohort study , 2018 .

[28]  D. McMillan,et al.  The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer. , 2014 .

[29]  Matti Pietikäinen,et al.  Identification of tumor epithelium and stroma in tissue microarrays using texture analysis , 2012, Diagnostic Pathology.

[30]  D. Kerr,et al.  Adjuvant chemotherapy versus observation in patients with colorectal cancer: a randomised study , 2007, The Lancet.