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Aims: After local excision of early rectal cancer, definitive lymph node status is not available. An alternative means for accurate assessment of recurrence risk is required to determine the most appropriate subsequent management. Currently used measures are suboptimal. We assess three measures of tumour stromal content to determine their predictive value after local excision in a well‐characterised cohort of rectal cancer patients without prior radiotherapy. Methods and results: A total of 143 patients were included. Haematoxylin and eosin (H&E) sections were scanned for (i) deep neural network (DNN, a machine‐learning algorithm) tumour segmentation into compartments including desmoplastic stroma and inflamed stroma; and (ii) digital assessment of tumour stromal fraction (TSR) and optical DNA ploidy analysis. 3′ mRNA sequencing was performed to obtain gene expression data from which stromal and immune scores were calculated using the ESTIMATE method. Full results were available for 139 samples and compared with disease‐free survival. All three methods were prognostic. Most strongly predictive was a DNN‐determined ratio of desmoplastic to inflamed stroma >5.41 (P < 0.0001). A ratio of ESTIMATE stromal to immune score <1.19 was also predictive of disease‐free survival (P = 0.00051), as was stromal fraction >36.5% (P = 0.037). Conclusions: The DNN‐determined ratio of desmoplastic to inflamed ratio is a novel and powerful predictor of disease recurrence in locally excised early rectal cancer. It can be assessed on a single H&E section, so could be applied in routine clinical practice to improve the prognostic information available to patients and clinicians to inform the decision concerning further management. [ABSTRACT FROM AUTHOR]

Background: Nuclear texture analysis gives information about the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image, providing texture features that may be used as quantitative tools for prognosis of human cancer. The aim of the study was to evaluate the prognostic value of adaptive nuclear texture features in early stage ovarian cancer. Methods: 246 cases of early stage ovarian cancer were included in the analysis. Isolated nuclei (monolayers) were prepared from 50 μm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices. A compact set of adaptive features was computed from these matrices. Results: Univariate Kaplan-Meier analysis showed significantly better relapse-free survival (p < 0.001) for patients with low adaptive feature values compared to patients with high adaptive feature values. The 10-year relapse-free survival was about 78% for patients with low feature values and about 52% for patients with high feature values. Adaptive features were found to be of independent prognostic significance for relapse-free survival in a multivariate analysis. Conclusion: Adaptive nuclear texture features from entropy matrices contain prognostic information and are of independent prognostic significance for relapse-free survival in early stage ovarian cancer. [ABSTRACT FROM AUTHOR]

Background: The clinical outcome for the individual prostate cancer patient is often difficult to predict, due to lack of reliable independent prognostic biomarkers. We tested DNA ploidy as a prognostic factor for clinical outcome in 186 patients treated with radical prostatectomy. Methods: DNA ploidy was measured using an automatic image cytometry system and correlated with preoperative PSA, age at surgery, Mostofi grade, surgical margins and Gleason score. Results: The mean follow up time after operation was 73.3 months (range 2–176 months). Of the 186 prostatectomies, 96 were identified as diploid, 61 as tetraploid and 29 as aneuploid. Twenty-three per cent, 36% and 62% of the diploid, tetraploid and aneuploid cases respectively, suffered from relapse during the observation time. DNA ploidy, Gleason score, Mostofi grading, surgical margins and preoperative PSA were all significant predictors of relapse in a univariate analysis. On multivariate analysis, only Gleason score and DNA ploidy proved to be independently predictors of disease recurrence. Furthermore, among the 68 cases identified with Gleason score 7, DNA ploidy was the only significant predictor of disease recurrence. Conclusions: Our data suggest that DNA ploidy should be included as an important additive prognostic factor for prostate cancer, especially for patients identified with Gleason score 7 tumours. [ABSTRACT FROM AUTHOR]

Aims: To determine the frequency and survival of the various types of uterine sarcoma in the total population of Norway and evaluate histopathological prognostic factors in order to identify risk groups. Methods and results: Histopathological review of all uterine sarcoma cases reported to the Norwegian Cancer Registry during 1970–2000 was undertaken. Survival dates were provided by The Cancer Registry. Kaplan–Meier survival curves were generated. The log rank test was used for univariate analysis and a Cox proportional hazards regression model for multivariate evaluation of survival. Stage of disease was the most important prognostic factor for all tumour types. Tumour size and the mitotic index (MI) were significant prognostic factors ( P < 0.0001) in leiomyosarcomas confined to the uterus and allowed for separation into three risk groups with marked differences in prognosis. The prognosis of endometrial stromal sarcomas confined to the uterus was related to MI ( P < 0.0001) and tumour cell necrosis ( P < 0.004). Combining these parameters allowed for separation into three risk groups with marked difference in prognosis. In adenosarcomas, tumour cell necrosis was the only significant prognostic factor. Conclusions: There are marked differences in survival between uterine sarcoma types. Leiomyosarcomas and endometrial stromal sarcomas can be divided into different groups. [ABSTRACT FROM AUTHOR]

In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images.The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images. [ABSTRACT FROM AUTHOR]

Controversy still exists regarding the validity of parameters commonly used in the evaluation of prognosis of patients with synovial sarcoma (SS). Forty-nine cases of previously untreated primary SS (23 females and 26 males, ranging in age from 7 to 81, with 31 tumors located in the lower extremity, 8 at the upper extremity and 10 at the trunchus), without regional lymph-node or distant metastases were studied. We investigated the relationship between (flow and image) DNA cytometry, proliferation activity, clinicopathologic parameters, and relapse-free and overall survival of the patients. The prognostic value of gender, age, duration of symptoms, location, compartmentalization, size, adequacy of surgical margins, residual tumor, adjuvant therapy, histologic subtype, extent of necrosis, glandular differentiation, calcification, and extent of hemangiopericytic areas, mitotic rate, amount of mast cells, blood vessel invasion, histologic (UICC and NCI) grades, DNA ploidy, percentage of cells in S and S+G2 phases, PCNA and Ki-67 labeling indices (LI), and TNM (UICC) stage of the tumors, were evaluated by univariate and multivariate (Cox hazard model) analyses. Short duration of symptoms (<12 months), biphasic SS, scarcity of mast cells ( [ABSTRACT FROM AUTHOR]

Background: The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after primary colorectal cancer resection. The aim of the present study was to develop a clinical decision support system based on DoMore-v1-CRC and pathological staging markers to facilitate individualised selection of adjuvant treatment.Methods: We estimated cancer-specific survival in subgroups formed by pathological tumour stage (pT<4 or pT4), pathological nodal stage (pN0, pN1, or pN2), number of lymph nodes sampled (≤12 or >12) if not pN2, and DoMore-v1-CRC classification (good, uncertain, or poor prognosis) in 997 patients with stage II or III colorectal cancer considered to have no residual tumour (R0) from two community-based cohorts in Norway and the UK, and used these data to define three risk groups. An external cohort of 1075 patients with stage II or III R0 colorectal cancer from the QUASAR 2 trial was used for validation; these patients were treated with single-agent capecitabine. The proposed risk stratification system was evaluated using Cox regression analysis. We similarly evaluated a risk stratification system intended to reflect current guidelines and clinical practice. The primary outcome was cancer-specific survival.Findings: The new risk stratification system provided a hazard ratio of 10·71 (95% CI 6·39-17·93; p<0·0001) for high-risk versus low-risk patients and 3·06 (1·73-5·42; p=0·0001) for intermediate versus low risk in the primary analysis of the validation cohort. Estimated 3-year cancer-specific survival was 97·2% (95% CI 95·1-98·4; n=445 [41%]) for the low-risk group, 94·8% (91·7-96·7; n=339 [32%]) for the intermediate-risk group, and 77·6% (72·1-82·1; n=291 [27%]) for the high-risk group. The guideline-based risk grouping was observed to be less prognostic and informative (the low-risk group comprised only 142 [13%] of the 1075 patients).Interpretation: Integrating DoMore-v1-CRC and pathological staging markers provided a clinical decision support system that risk stratifies more accurately than its constituent elements, and identifies substantially more patients with stage II and III colorectal cancer with similarly good prognosis as the low-risk group in current guidelines. Avoiding adjuvant chemotherapy in these patients might be safe, and could reduce morbidity, mortality, and treatment costs.Funding: The Research Council of Norway. [ABSTRACT FROM AUTHOR]