Breast cancer outcome prediction with tumour tissue images and machine learning

Vol.:(0123456789) 1 3 Breast Cancer Research and Treatment (2019) 177:41–52 https://doi.org/10.1007/s10549-019-05281-1 PRECLINICAL STUDY Breast cancer outcome prediction withtumour tissue images andmachine learning RikuTurkki1,11 · DmitriiByckhov1· MikaelLundin1· JormaIsola2· StigNordling3· PanuE.Kovanen4· ClareVerrill5,6· KarlvonSmitten7· HeikkiJoensuu8· JohanLundin1,9· NinaLinder1,10 Received: 10 December 2018 / Accepted: 16 May 2019 / Published online: 22 May 2019 © The Author(s) 2019 Abstract Purpose Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input. Methods Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients. Results In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33–3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20–3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55–0.65), as compared to 0.58 (95% CI 0.53–0.63) for human expert predictions based on the same TMA samples. Conclusions Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer. Keywords Breast cancer· Machine learning· Deep learning· Outcome prediction Abbreviations AUC Area under receiver operating characteristics curve CI Confidence interval CNN Convolutional neural network Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1054 9-019-05281 -1) contains supplementary material, which is available to authorized users. * Riku Turkki rik[email protected] 1 Institute forMolecular Medicine Finland (FIMM), University ofHelsinki, Helsinki, Finland 2 Department ofCancer Biology, BioMediTech, University ofTampere, Tampere, Finland 3 Department ofPathology, Medicum, University ofHelsinki, Helsinki, Finland 4 HUSLAB andMedicum, Helsinki University Hospital Cancer Center andUniversity ofHelsinki, Helsinki, Finland 5 Nuffield Department ofSurgical Sciences, University ofOxford, Oxford, UK 6 NIHR Oxford Biomedical Research Centre, Oxford, UK 7 Eira Hospital, Helsinki, Finland 8 Department ofOncology, Helsinki University Hospital andUniversity ofHelsinki, Helsinki, Finland 9 Department ofPublic Health Sciences, Karolinska Institutet, Stockholm, Sweden 10 Department ofWomen’s andChildren’s Health, International Maternal andChild health (IMCH), Uppsala University, Uppsala, Sweden 11 Science forLife Laboratory (SciLifeLab), Karolinska Institutet, Solna, Sweden 42 Breast Cancer Research and Treatment (2019) 177:41–52 1 3 DSS Disease-specific survival DRS Digital risk score ECW Enhanced wavelet compression ER Oestrogen receptor GMM Gaussian mixture model HER2 Human epidermal growth factor receptor 2 HR Hazard ratio IFV Improved Fisher vector OS Overall survival PCA Principal component analysis PR Progesterone receptor SVM Support vector machine TIL Tumour-infiltrating lymphocytes TMA Tissue microarray Background There is a growing interest around the potential of machine learning to improve the accuracy of medical diagnostics [1]. Novel machine learning techniques have not only advanced the state-of-the-art in several pattern recognition tasks [2, 3], but also have the potential to extract clinically relevant information from complex medical imaging data sets. Especially, methods using deep learning have been successful in various medical image analysis tasks [4, 5], some of them reaching performance of experienced specialists in individual diagnostic tasks [6, 7]. Within pathology, whole-slide scanners have enabled accurate digitisation of histological samples with sub-micrometre resolution allowing for computerised analysis of the specimens with machine learning algorithms [8]. Computerised methods for detection of mitoses [9–11], infiltrating immune cells [12, 13] and other tissue entities such as segmentation of epithelial and stromal tissue compartments or discrimination between viable and non-viable tissue [14–18] have been studied. Recent reviews [5, 8, 19] offer thorough summaries on methods developed for analysis of histological samples. Specific type of deep learning methods, convolutional neural networks (CNNs), is frequently used in the development of the image-based classifiers. CNNs are composed of consecutive, interconnected and hierarchical stages—an architecture inspired by the structure of biological neural networks [20]—making it a powerful tool to capture abstract patterns in visual data. Utility of CNNs was recently demonstrated in detection of breast cancer metastases in lymph node tissue sections [21]. Instead of predicting patient outcome based on intermediate quantification of tissue structures, such as specific cell types and states (e.g. mitotic cells, pleomorphic cells, immune cells) or tissue structures and entities (ducts, necrosis, vessels), our aim in this study is to predict patient outcome based solely on the visual appearance of the breast cancer tissue without any prior assumptions. We hypothesise that a method capable of inferring relevant signals for outcome without prior knowledge of tissue structures may be able to reveal complementary and unbiased prognostic information. Materials andmethods Patients andpreparation oftumour tissue microarrays We pooled two data sets for the study, the FinProg series and a similar single-centre series from Helsinki University Central Hospital. The FinProg series (N = 1860) is a nationwide cohort including approximately 50% of all women diagnosed with breast cancer in Finland 1991 and 1992 [22] and cover most of the patients (93%) within five selected geographical regions (the FinProg Breast Cancer Database1). The other patient series(N = 527) consists of patients diagnosed mainly in the Helsinki region and treated at the Department of Surgery and Oncology, Helsinki University Hospital, from 1987 to 1990. Both series include comprehensive information on clinical and pathologic characteristics extracted from the hospital and laboratory records. In the current study, we used information on histological grade and type, tumour size, number of positive lymph nodes, patient age, as well as oestrogen (ER), progesterone (PR) and human epidermal growth factor receptor 2 (HER2) status. In addition, we had information on treatment type given; 62% of the patient received local therapy and 42% systemic therapy. Routinely fixed paraffin-embedded breast cancer samples were retrieved from the archives of pathology laboratories, and representative tumour regions identified for preparation of TMA blocks [23]. From the tissue blocks available, three representative 0.60mm tissue cores were punched and assembled into 23 TMA blocks, each containing 50–144 tumour tissue cores. Immunohistochemistry, chromogen insitu hybridisation, as well as grading [24] were performed as previously described [22]. Inclusion criteria for the current study were the following: survival data with cause of death, images of the breast tumour tissue, as well as a tissue sample area > 0.02mm2 (corresponding to 400,000 pixels in the image). Patients with lobular or ductal carcinoma insitu, synchronous or metachronous bilateral breast cancer or other malignancy (except for basal cell carcinoma or cervical carcinoma insitu), distant metastasis, or who did not undergo surgery of the primary tumour were excluded. In addition, the TMAs 1 http://www.finpr og.org. 43Breast Cancer Research and Treatment (2019) 177:41–52 1 3 that were checked for quality and non-representative, samples without tumour tissue, were excluded. After exclusions, 1299 tissue spots, one per patient, were available for further analysis. Lastly, the spots were randomly divided into separate training (N = 868, 67%) and test (N = 431, 33%) sets. Compared to the commonly used 80–20% split, we assigned more samples (33%) to the test set in order to also enable subgroup and multivariate analyses. The median follow-up of patients in the final patient cohort alive at the end of follow-up period is 15.9years (range 15.0–20.9, interquartile range 15.4–16.3years). Image acquisition Five-micrometre thick sections were cut from the TMA blocks, stained with haematoxylin and eosin and digitised with a whole-slide scanner (Pannoramic 250 FLASH, 3DHISTECH Ltd., Budapest, Hungary) equipped with a 20 × objective (numerical aperture 0.80) and a 1 × adapter, and a progressive scan colour camera with three separate charge-coupled devices with 1618 × 1236 pixels sized 4.40μm × 4.40μm (CIS_VCC_F52U25CL, CIS Corporation, Tokyo, Japan) resulting in an image where one pixel represents an area of 0.22μm × 0.22μm. Images were stored in a whole-slide image format (MRX, 3DHISTECH Ltd., Budapest, Hungary) and further compressed to a wavelet file format (Enhanced Compressed Wavelet, ECW, ER Mapper, Intergraph, Atlanta, GA) with a compression ratio of 1:10. The compressed virtual slides were uploaded to a wholeslide image management server (WebMicroscope, Aiforia Technologies Oy, Helsinki, Finland) where individual images of TMA spots were segmented from the whole-slide image and downloaded for algorithm training and testing as uncompressed portable network graphics files. Outcome classification We extracted local convolutional image descriptors for each TMA spot image by reading the activations from the last convolutional layer of convolutional neural network (VGG-16) trained on the ImageNet database [25], and used improved Fisher vector (IFV) encoding [26] to aggregate the descriptors from the image foreground regions into a single descriptor. The network used (VGG-16) is a 16-layer network with small 3 × 3 convolutional filters. The network was not trained or fine-tuned on our data set, instead we used it only as a feature extractor. A benefit of the descriptor aggregation approach is that an image of arbitrary size can be given as an input for the model. In addition, a study [27] showed that descriptor aggregation with IFV might yield stronger discrimination when compared to fully connected layers. For computation of the IFV, the convolutional image descriptors were compressed with principal component analysis (PCA) from 512 channels into 16 components, and 64 mixture components were used in quantising the data with a Gaussian mixture model (GMM). We defined the image foreground regions by applying Otsu’s thresholding [28] to a transformed spot image It , when It =(ln ( 1−I g) +2e)∕2 e , and where Ig is a Gaussian-filtered (radius of 15 pixels) grayscale version (averaged over colour channels) of the spot image. After the thresholding, all objects smaller than 12,500 pixels in area were removed. Finally, we compressed the IFV descriptors with PCA into 48 bins before classification with a linear support vector machine (SVM). The analysis pipeline was implemented in a numerical computing environment (MATLAB R2016b, MathWorks, Natick, MA, U.S.) using libraries for computer vision and machine learning [29–31]. For training the DRS group classifier, we defined two categories according to the patients’ survival status and follow-up time. In the first category (high risk, N = 340), we included all the patients who died due to breast cancer earlier than 10years after the diagnosis, and in the other category (low risk, N = 528), we included the patients who did not die of breast cancer during the entire follow-up time. For learning the unsupervised IFV encoding, we randomly sub-sampled 4 × 106 local image descriptors from the training set. The sampling was balanced between low-risk and high-risk spots. In training the SVM model, we used 868 breast cancer TMA spot images, each spot representing an individual patient. Visual risk scoring Three pathologists scored the test set TMA spot images into low and high-risk groups using a web-based viewing and annotation software (WebMicroscope, Aiforia Technologies Oy, Helsinki, Finland). Prior and during the visual scoring, the pathologists were able to view the training set TMA spots grouped as they were labelled in training of the SVM classifier. Based on the pathologists’ scoring, a visual risk score (high risk or low risk) was formed with majority voting. Furthermore, one pathologist assessed the following tissue entities in each TMA spot: mitoses (0 vs. 1 vs. > 1), pleomorphism (minimal vs. moderate vs. marked), tubules (≤ 10 vs. 10–75 vs. > 75%), necrosis (absent vs. present) and quantity of tumour-infiltrating lymphocytes (TILs) (low vs. high). Statistical analysis The Kaplan–Meier method was used for estimating the survival function [32] and the log-rank test was used in comparison of survival curves. The disease-specific survival (DSS) time was defined as the time period between date of diagnosis and death of breast cancer, censoring patients who were 44 Breast Cancer Research and Treatment (2019) 177:41–52 1 3 alive on the date of the last contact, and those who had died from another cause on the date of death. The overall survival (OS) time was defined as the time period between the date of breast cancer diagnosis and death of any cause, censoring patients alive on the date of the last contact. For estimating the effect size (hazard ratio, HR) while accounting for the effect of other covariates, we used the Cox proportional hazard model [33]. C-index (concordance) and AUC were used to evaluate the discrimination and prediction accuracy of survival models [34]. Chi-squared contingency table test was used for comparison of categorical variables, and continuous distributions were compared with Kruskal–Wallis test. All statistical analyses with a two-sided p value lower than 0.05 were considered significant. Results Outcome classification We trained the outcome classifier using a training set of 868 tumour tissue images, and subsequently classified the test set representing 431 breast cancer patients into low and high DRS groups (Fig.1). In the test set, 237 (55%) patients were classified into the low DRS group and 194 (45%) patients into the high DRS group. The patient characteristics are summarised in Table1. The DRS model performance rates measured with area under receiver operating characteristics curve (AUC) on the test and training sets were 0.58 and 0.63, respectively, indicating no substantial model overfitting (Supplementary Fig. S1). Outcome classification andclinicopathological variables Patients predicted to have an increased risk of breast cancerspecific death had significantly greater proportion of highgrade tumours (p = 0.014) as compared to patients who were assigned to the low DRS group (Table1). Moreover, patients in the high DRS group had larger tumours (p < 0.001), higher number of positive lymph nodes (p = 0.003) and were more often PR-negative (p = 0.015). Outcome classification andsurvival analysis We investigated the prognostic value of the DRS grouping with univariable and multivariable survival analysis in the test set. Women in the lower DRS group had more favourable breast cancer-specific (p < 0.001) and overall survival (p = 0.003) (Fig.2). Ten-year DSS in the low DRS group was 82% (95% CI 77–87%) compared to 65% (95% CI 58–73%) in the high DRS group. When the cancers were split according to histological grade assessed from original whole-slide samples, the DRS grouping showed the strongest discrimination in grade I cancer (P < 0.001), whereas the differences observed in grade II (p = 0.410) and grade III (p = 0.083) groups were not statistically significant (Fig.3). When the cancers were divided according the steroid hormone receptor status, the DRS classifier was a significant predictor of survival both in the ER positive (p = 0.025) and ER negative (p < 0.001) subgroups. The DRS grouping was a significant predictor in the PR-negative subgroups, but not in the subset of PR-positive breast cancer (p = 0.003). Furthermore, Training set, (N=868) Test set, (N=431) Time Survival % Low risk High risk Model parameters are learned from the training data only. Survival analysis of the risk group classification in the test set. Low DRS High DRS Feature extraction with a deep CNN Feature pooling with IFV and PCA High risk Low risk Risk group classification with SVM The computational pipeline. Fig. 1 Workflow for training and testing the digital risk score (DRS) classification. The computational pipeline consists of three sequential steps: (i) feature extraction with a deep convolutional neural network (CNN), (ii) feature pooling with improved Fisher vector encoding (IFV) and principal component analysis (PCA) and (iii) classification with support vector machine (SVM). Training set samples are used in supervision and a separate test set-up used in validation 45Breast Cancer Research and Treatment (2019) 177:41–52 1 3 the risk grouping was a significant predictor for survival both among HER2 negative (p = 0.015) and positive patients (p < 0.001). Subgroup analysis according to tumour size and nodal status are shown in Figure4. A multivariate survival analysis showed that classification into the high DRS group was associated with unfavourable prognosis (HR = 2.04, 95% CI 1.20–3.44, p = 0.007), and indicated that the DRS grouping is an independent predictor for survival (Table2). Tumour size (1.04, 95% CI 1.02–1.06, p < 0.001), PR positivity (0.42, 95% CI 0.25–0.71, p < 0.001) and having > 10 positive lymph nodes (HR = 4.74, 95% CI 1.17–19.30, p < 0.029) were also independent predictors. Table 1 Patient characteristics Left association of clinicopathological variables in the training and test sets. Right: association of clinicopathological variables between patients in low and high digital risk score (DRS) groups p-values < 0.05 are shown in bold Variables Whole data set (N = 1299) Test set (N = 431) Training set (N = 868) Training set (N = 431) p Value Low DRS (N = 237) High DRS (N = 194) p Value %N%N%N%N Number of positive lymph nodes Mean 1.4 1.2 0.407 0.9 1.6 0.003 0 58 504 59 253 0.323 63 150 53 103 0.057 1–3 24 206 23 99 23 54 23 45 4–9 8 73 9 38 6 15 12 23 > 10 3 30 2 7 1 2 3 5 Unknown 6 55 8 34 7 16 9 18 Tumour size (per mm) Mean 23.7 23.2 0.817 2.15 25.3 <0.001 Unknown 3 28 5 22 5 13 5 9 Histological grade I 16 143 19 83 0.086 23 54 22 43 0.014 II 34 296 36 154 32 75 41 79 III 23 197 18 76 14 33 22 43 Unknown 27 232 27 118 32 75 22 43 Histological type Ductal 76 662 77 333 0.742 74 175 81 158 0.079 Lobular/special 24 206 23 98 26 62 19 36 Age ≤ 39 7 63 7 30 0.353 9 21 5 9 0.140 40–49 21 186 24 103 27 64 20 39 50–59 27 234 22 94 21 49 23 45 60–69 20 172 21 91 20 47 23 44 ≥ 70 25 213 26 113 24 56 29 57 ER Negative 29 248 27 116 0.572 25 60 29 56 0.443 Positive 62 538 64 274 65 155 61 119 Unknown 9 82 10 41 9 22 10 19 PR Negative 42 362 41 177 0.803 36 86 47 91 0.015 Positive 49 423 50 215 56 132 43 83 Unknown 10 83 9 39 8 19 10 20 HER2 Negative 72 623 74 321 0.713 76 181 72 140 0.136 Positive 17 146 16 70 14 32 20 38 Unknown 11 99 9 40 10 24 8 16 46 Breast Cancer Research and Treatment (2019) 177:41–52 1 3 Outcome classification andvisual risk score Out of the 431 test TMA spot images, 109 were classified by at least one pathologist as not evaluable due to insufficient amount of cancer tissue or partial spot detachment for reliable risk assessment and were therefore left out from the analyses. In the remaining subset of 322 spot images, 60% (N = 193) of the patients were assigned to the low-risk and 40% (N = 129) to the high-risk group according the majority vote visual risk score, as compared to 55% (N = 177) low risk and 45% (N = 145) high risk according to the DRS groups. Percent agreement between the pathologists’ individual scorings was 32%. There was a significant agreement between pathologist 1 and 3 (κ(1,3) = 0.27; p < 0.001), but not between pathologist 1 and 2 (κ(1,2) = 0.005; p = 0.931) or pathologist 2 and 3 (κ(2,3) = − 0.028; p = 0.598) in assigning the patients into lowand high-risk groups. In a univariate analysis, the digital risk score was found to be a significant predictor of disease-specific survival with a HR = 2.10 (95% CI 1.40–3.18, p < 0.001) and C-index of 0.60 (95% CI 0.55–0.65). Similarly, the visual risk score was found to be a significant predictor of survival with a HR = 1.74 (95% CI 1.16–2.61, p = 0.006) and C-index of 0.58 (95% CI 0.53–0.63) (Supplementary Fig.2). Interestingly, a Chi-square test of these univariate survival models indicated that the models were significantly different (p < 0.001). When the visual risk score and the DRS group were both included as covariables in a multivariate survival analysis, both turned out to be independent predictors (HR = 2.05, p < 0.001 for the DRS and HR = 1.68, p = 0.012 for the visual risk score). C-index of the combined survival model was 0.64 (95% CI 0.58–0.69). An analysis of the association between cancer morphological features and the digital risk score showed that the DRS was significantly correlated with nuclear pleomorphism and tissue tubule formation, whereas the visual risk score was significantly associated also with cancer mitotic count, presence of necrosis and the number of TILs (Supplementary Table1). Discussion We found that by utilising machine learning algorithms it is possible to extract information relevant for breast cancer patient outcomes from tumour tissue images stained for the basic morphology only. Importantly, the results show that prognostic discrimination is achievable without guidance or the use of prior knowledge of breast cancer biology or pathology in the training of the algorithm. Instead of directing the focus towards cancer cellular features (e.g. number of mitotic cells, immune cells, pleomorphic cells) or tissue entities (e.g. duct formation, presence of tumour necrosis), we guided the supervision simply with the patient survival outcome data. Computerised methods for analysing breast tissue images for patient prognostication have been studied earlier. Extracting more than 6000 predefined image features from two Fig. 2 Disease-specific survival (DSS) and overall survival (OS) according the classification into low and high digital risk score (DRS) groups 47Breast Cancer Research and Treatment (2019) 177:41–52 1 3 Fig. 3 Disease-specific survival (DSS) according the classification into low and high digital risk score (DRS) groups 48 Breast Cancer Research and Treatment (2019) 177:41–52 1 3 cohorts (N = 248 and N = 328), authors in [35] showed that it is feasible to learn an outcome predictor for overall survival (HR = 1.78, p = 0.017) using small tumour regions. Using a subset of the whole slides from [36], an earlier study [37] proposed a joint analysis of image features and gene expression signatures for prognostic biomarker discovery. The authors used a training set of 131 patients and validated the biomarkers with H&E-stained tumour samples from 65 breast cancer patients. The strongest predictive image feature the authors identified reached a HR = 1.7 (p = 0.002) in prediction of relapse-free survival. Moreover, a previous work [38] identified morphological features in a data set of 230 breast cancer patients that were independent and prognostic for 8-year disease-free survival. Our study extends this body of work by demonstrating that it is possible to learn a prognostic signal from a patient cohort without domain knowledge. Our analysis was blinded from the fundamental concepts such as cells and nuclei, different tissue compartments and histological grade that were incorporated in the previous studies. Nevertheless, we were able to train an independent risk predictor based on the training cohort, using the raw image data and follow-up information only. Furthermore, we Fig. 4 Disease-specific survival (DSS) according the classification into low and high digital risk group (DRS) groups in patients with different tumour size and nodal status 49Breast Cancer Research and Treatment (2019) 177:41–52 1 3 used a large multicentre cohort with a median follow-up time of over 15years and analysed the associations of the outcome predictor with the commonly used clinicopathological variables. Outside breast cancer, direct outcome prediction using tissue morphology has been successfully applied in colorectal cancer [39] and glioma [40]. Moreover, we compared the DRS group with the visual risk score, which combined three pathologists’ risk assessments according to a majority vote rule. Even though Table 2 Cox uniand multivariate survival analysis Histological grade and type were assessed from whole tumour sections, while ER, PR and HER2 were assessed from TMAs. In order to meet the Cox proportionality assumption, ER was left out from the multivariate analysis and grade II and III were combined ER estrogen receptor status, PR progesterone receptor status, HER2 human epidermal growth factor receptor 2 gene amplification p-values < 0.05 are shown in bold Variables Univariate analysis Multivariate analysis HR CI 95% p value HR CI 95% p Value DRS Low Ref. Ref. High 2.10 (1.33–3.32) 0.001 2.04 (1.20–3.44) 0.007 Number of positive lymph nodes 0 Ref. Ref. 1–3 1.53 (0.89—2.63) 0.123 2.12 (0.83–1.47) 0.116 4–9 2.93 (1.61–5.33) <0.001 2.15 (0.75–6.19) 0.154 > 10 7.43 (2.90–19.02) <0.001 4.75 (1.17–19.30) 0.029 Tumour size Per mm 1.04 (1.03–1.06) <0.001 1.04 (1.02–1.06) <0.001 Histological grade I Ref. Ref. II or III 3.14 (1.61–6.09) <0.001 1.57 (0.76–3.20) 0.220 Histological type Ductal Ref Ref. Lobular/special 0.73 (0.40–1.33) 0.306 0.90 (0.41–1.95) 0.782 Age ≤ 39 Ref. Ref. 40–49 0.78 (0.33–1.88) 0.585 0.43 (0.17–1.12) 0.084 50–59 0.69 (0.28–1.70) 0.425 0.48 (0.19–1.28) 0.144 60–69 1.00 (0.42–2.36) 0.996 0.62 (0.25–1.57) 0.319 ≥ 70 1.58 (0.66–3.79) 0.306 1.35 (0.49–3.72) 0.564 ER Negative Ref. Positive 0.69 (0.44–1.09) 0.15 PR Negative Ref. Ref. Positive 0.34 (0.21–0.55) < 0.001 0.42 (0.25–0.71) 0.001 HER2 Negative Ref. Positive 1.51 (0.90–2.53) 0.119 1.07 (0.57–1.98) 0.831 Systematic therapy Not given Ref. Ref. Given 1.90 (1.21–2.98) 0.005 1.07 (0.22–1.47) 0.245 Local therapy Not given Ref. Ref Given 1.23 (0.75–2.03) 0.404 1.22 (0.61–2.46) 0.571