Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.

BACKGROUND: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. METHODS: More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. FINDINGS: 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72-5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07-4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. INTERPRETATION: A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes. FUNDING: The Research Council of Norway.

Chromatin changes predict recurrence after radical prostatectomy.

BACKGROUND: Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei. METHODS: A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7-14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy. RESULTS: Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1-5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5-4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7. CONCLUSION: A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy.

Computerized image analysis of the Ki-67 proliferation index in mantle cell lymphoma.

AIMS: Manual counting of the fraction of Ki-67-positive cells (the Ki-67 index) in 1000 tumour cells is considered the 'gold standard' to predict prognosis in mantle cell lymphoma (MCL). This time-consuming method is replaced by the faster, but less accurate, semiquantitative estimation in routine practice. The aim of this study was to investigate the use of computerized image analysis software for scoring of Ki-67 in MCL. METHODS AND RESULTS: We developed an automated method for determining the Ki-67 index by computerized image analysis and tested it using a cohort of 62 MCL patients. The data were compared to Ki-67 scores obtained by semiquantitative estimation and image-based manual counting. When using the Ki-67 index as a continuous parameter, both image-based manual counting and computerized image analysis were related inversely to survival (P = 0.020 and P = 0.025, respectively). Ki-67 index obtained by semiquantitative estimation was not associated significantly with survival (P = 0.093). The results were validated in a second patient cohort with similar results. CONCLUSION: Computerized image analysis of the Ki-67 index in MCL is an attractive alternative to semiquantitative estimation and can be introduced easily in a routine diagnostic setting for risk stratification in MCL.

Association Between Proportion of Nuclei With High Chromatin Entropy and Prognosis in Gynecological Cancers.

Background: Nuclear texture analysis measuring differences in chromatin structure has provided prognostic biomarkers in several cancers. There is a need for improved cell-by-cell chromatin analysis to detect nuclei with highly disorganized chromatin. The purpose of this study was to develop a method for detecting nuclei with high chromatin entropy and to evaluate the association between the presence of such deviating nuclei and prognosis. Methods: A new texture-based biomarker that characterizes each cancer based on the proportion of high-chromatin entropy nuclei (<25% vs ≥25%) was developed on a discovery set of 175 uterine sarcomas. The prognostic impact of this biomarker was evaluated on a validation set of 179 uterine sarcomas, as well as on independent validation sets of 246 early-stage ovarian carcinomas and 791 endometrial carcinomas. More than 1 million images of nuclei stained for DNA were included in the study. All statistical tests were two-sided. Results: An increased proportion of high-chromatin entropy nuclei was associated with poor clinical outcome. The biomarker predicted five-year overall survival for uterine sarcoma patients with a hazard ratio (HR) of 2.02 (95% confidence interval [CI] = 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR = 2.91, 95% CI = 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR = 3.74, 95% CI = 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR = 1.81, 95% CI = 1.21 to 2.70, for sarcoma; HR = 1.71, 95% CI = 1.01 to 2.90, for ovarian cancer; and HR = 2.03, 95% CI = 1.19 to 3.45, for endometrial cancer). Conclusions: A novel method detected high-chromatin entropy nuclei, and an increased proportion of such nuclei was associated with poor prognosis. Chromatin entropy supplemented existing prognostic markers in multivariable analyses of three gynecological cancer cohorts.

Changes in Chromatin Structure in Curettage Specimens Identifies High-Risk Patients in Endometrial Cancer.

BACKGROUND: Most endometrial carcinoma patients are diagnosed at an early stage with a good prognosis. However, a relatively low fraction with lethal disease constitutes a substantial number of patients due to the high incidence rate. Preoperative identification of patients with high risk and low risk for poor outcome is necessary to tailor treatment. Nucleotyping refers to characterization of cell nuclei by image cytometry, including the assessment of chromatin structure by nuclear texture analysis. This method is a strong prognostic marker in many cancers but has not been evaluated in preoperative curettage specimens from endometrial carcinoma. METHODS: The prognostic impact of changes in chromatin structure quantified with Nucleotyping was evaluated in preoperative curettage specimens from 791 endometrial carcinoma patients prospectively included in the MoMaTEC multicenter trial. RESULTS: Nucleotyping was an independent prognostic marker of disease-specific survival in preoperative curettage specimens among patients with Federation Internationale des Gynaecologistes et Obstetristes (FIGO) stage I-II disease (HR=2.9; 95% CI, 1.2-6.5; P = 0.013) and significantly associated with age, FIGO stage, histologic type, histologic grade, myometrial infiltration, lymph node status, curettage histology type, and DNA ploidy. CONCLUSIONS: Nucleotyping in preoperative curettage specimens is an independent prognostic marker for disease-specific survival, with potential to supplement existing parameters for risk stratification to tailor treatment. IMPACT: This is the first study to evaluate the prognostic impact of Nucleotyping in curettage specimens from endometrial carcinoma and shows that this may be a clinically useful prognostic marker in endometrial cancer. External validation is warranted. Cancer Epidemiol Biomarkers Prev; 26(1); 61-67. ©2016 AACR.