Machine learning, and in particular deep learning, is expected to transform many areas of medicine due to its unmatched capability to make accurate and objective predictions [1
]. These methods have proven particularly useful in medical image analysis and have great potential to improve the assessment of diagnostic and prognostic biomarkers in terms of efficiency and reproducibility [2
]. Convolution neural networks (CNNs) are a fundamental class of deep learning networks that can be trained to detect, segment, and classify objects using large learning data sets [1
]. CNNs are well-suited to perform complex visual recognition tasks, such as tumor detection, Gleason grading [4
], scoring of tissue stains [6
], as well as determining prognosis [8
], and are emerging as a core method in medical image analysis.
Localized prostate cancer (PCa) is a heterogeneous disease with a highly variable clinical outcome [9
]. Although several useful prognostic tools combining clinicopathological parameters are available, additional objective biomarkers are needed to further improve risk stratification [10
]. Currently, no molecular tissue-based PCa biomarker is recommended for routine clinical use [11
Chromosomal instability (CIN)—a high rate of loss or gain of whole or parts of chromosomes—is a form of genomic instability observed in most human cancers. It is associated with intratumor heterogeneity and a more aggressive cancer phenotype [12
]. CIN status can be inferred from measurements of DNA ploidy (cellular DNA content), which is a prognostic biomarker in PCa (reviewed in [14
]). DNA ploidy status is best assessed using DNA image cytometry, where the correct and reproducible subclassification of nuclei can be provided by a machine learning-based method. However, the resolution of DNA image cytometry is insufficient to detect additions or deletions of small chromosomal fragments. Loss of the phosphatase and tensin homolog (PTEN) tumor suppressor gene is one of the most common genomic alteration in PCa, and it has been consistently reported to be associated with adverse clinical outcomes (reviewed in [15
]). Lennartz et al. demonstrated that combining assessment of DNA ploidy by flow cytometry and deletions of PTEN and 6q15 by fluorescence in situ hybridization (FISH) provided an independent prognostic biomarker in a large cohort of PCa patients. Since PTEN protein loss is highly concordant with gene deletion, PTEN status can be readily obtained by immunohistochemistry (IHC), which is more feasible to adapt to the pathology workflow compared to FISH [16
]. However, manual scoring of IHC-stained slides is very time consuming and subjective, and the published computer-aided PTEN scoring methods [17
] are inadequate to mitigate these issues.
The aim of this study was to develop a fully automated method for PTEN scoring of IHC-stained slides using CNNs and to determine its prognostic value in patients treated with radical prostatectomy (RP). The method was trained and internally tested using a discovery cohort and validated in an independent cohort according to a predefined protocol that precisely described the primary analysis. As a secondary analysis, we investigated whether combining the automatic PTEN assessment with automatically assessed DNA ploidy status would improve prognostication.
2. Materials and Methods
The discovery and validation cohorts were both comprised of patients with PCa who underwent RP at the Norwegian Radium Hospital, Oslo, a tertiary comprehensive cancer center in Norway. The patients in the two cohorts were operated on by different surgeons at largely disjointed time periods (46 out of the 512 (9%) patients were operated on during the overlapping time period) and in general using different surgical techniques. According to the convention in the medical statistics community, such an approach represents a type of external validation called narrow validation, which may be considered intermediate between broad and internal validation [19
]. Each prostate gland was processed into a series of 3–5 mm thick formalin-fixed, paraffin-embedded tissue blocks. Both cohorts are described in detail in the study protocol (File S1 page 1–5). The study was approved by the Norwegian Regional Committees for Medical Research Ethics South-East region (REK numbers S-07443a and 2013/476). Gleason scores (GSs) of the tumors were assessed in the clinical routine for all patients in the validation cohort. All study specimens were centrally reviewed, at different time points, by an experienced uropathologist (LV) using the updated 2005 International Society of Urological Pathology (ISUP) guidelines [20
] in the discovery cohort and the 2014 ISUP guidelines [22
] in the validation cohort. The definitions of Gleason grade patterns in the updated 2005 and 2014 ISUP consensus are similar. The only difference is the recommendations on grading of glomeruloid glands, which are an extremely rare feature in prostate tumors [23
]. Gleason scores were classified into Gleason grade groups (GGGs) [22
2.2. Discovery Cohort and Test Subset
Of the 317 patients operated on with open retropubic prostatectomy between 1987 and 2005 by one surgeon (HW), 10 were excluded due to preoperative therapy (n = 1), death from postoperative complications (n = 1), loss to follow-up (n = 1), or no tumor material available (n = 7).
A subset of 185 blocks from 93 non-excluded patients was used to develop a CNN to detect the tumor region in which the PTEN score was assessed. This subset was randomly split on the patient level into a train subset containing 70% of the patients and a tune subset containing the other 30%. The train subset contained 129 blocks from 65 patients and was used to train the tumor detector. The tune subset contained 56 blocks from 28 patients and was used to select model hyperparameters, in particular to determine when to cease training.
Another CNN was developed to detect and segment tumor cells and classify them as PTEN-positive or PTEN-negative. This development used a subset of 34 blocks from 34 patients, which were randomly split into a train and a tune subset, again targeting a 70:30 split. The resulting train subset contained 24 blocks, and the tune subset contained the remaining 10 blocks.
A test subset of 253 non-excluded patients with three available tumor-containing blocks was used to evaluate the performance of the automatic PTEN scoring method (protocol page 30–34). None of the 34 patients used for developing the PTEN classifier were included in the test subset, whereas 50 patients were included in both the dataset used for developing the tumor detector (i.e., the 93 patients) and the test subset. Different thresholds for dichotomizing the automatic PTEN scores were evaluated in the test subset (i.e., the 253 patients), and the decision to use 50% as the threshold in the validation was based on these results (protocol page 33–34).
2.3. Validation Cohort
Of the 287 patients operated on with open retropubic prostatectomy (n = 75) or robot-assisted prostatectomy (n = 182) between 2001 and 2006 by one surgeon (BB), 28 were excluded due to missing patient consent (n = 21), missing or less than six weeks of follow-up (n = 4), and no tumor material available (n = 3). Three tumor-containing blocks were analyzed for each of the 259 eligible patients.
2.4. Immunohistochemistry, Scanning of Tissue Slides, and Manual PTEN Scoring
Monoclonal PTEN antibody (1:400, 138G6, Cell Signaling Technology, Danvers, MA, USA) was applied on 3 μm tissue sections after heat-induced epitope retrieval, as previously described [24
]. IHC-stained slides were scanned on a NanoZoomer XR slide scanner (Hamamatsu Photonics, Hamamatsu, Japan) at the highest resolution available (termed 40x). The resulting whole-slide images (WSIs) typically contained an order of 100.000 × 100.000 pixels, each representing a physical size of 0.227 × 0.227 µm. All the WSIs were quality controlled, and slides were rescanned if they were out of focus. PTEN expression was manually scored at 10% intervals by two observes (Karolina Cyll (KC) and Elin Ersvær), blinded to clinicopathological data. Cells were considered PTEN-negative if the cytoplasmic and nuclear staining was absent or decreased compared with internal positive controls (benign glands and/or stroma), as previously described [16
]. PTEN expression was not scored when the intensity of the staining was weak or absent in the internal positive controls or when ≥95% of the tumor area had fallen off during sample preparation. The correlation between the manual scores obtained by the two observers was strong (Pearson’s r = 0.916, 95% CI 0.903 to 0.927). Survival analysis of each observer’s PTEN scores is presented in Figure S1
. A consensus score was used in further analyses.
2.5. DNA Image Cytometry
Preparation of nuclear monolayers was performed according to a modified Hedley method [26
]. Identification of representative epithelial and stromal (reference) nuclei and DNA ploidy histogram classification into diploid, tetraploid, or aneuploid was done automatically using PWS Classifier software (Room4 Ltd., Sussex, UK). The software makes use of support vector machines, a machine learning technique, trained with manual cell classifications as references to discard non-intact nuclei (i.e., cut, folded, and connected) and to classify cell types based on morphological features and pixel-based image metrics extracted from the cell images [27
] (see File S1 page 9–10 for details).
2.6. Automatic PTEN Scoring
The automatic scoring method consisted of three steps. First, each WSI was partitioned into smaller, non-overlapping regions, called tiles, measuring 800 × 800 pixels. Next, each tile was classified as tumor or non-tumor by the tumor detector. Finally, the PTEN classifier detected and segmented tumor cells in the remaining tumor tiles and classified them as PTEN-positive or PTEN-negative. The entire system thus provided a count of PTEN-positive and PTEN-negative tumor cells without any human interaction (Figure 1
). The PTEN score for a WSI was calculated as the ratio between the number of positive cells and the total number of positive and negative cells. The score for a patient was calculated as the average score of all its WSIs.
The training and tuning of the tumor detector and PTEN classifier are described in detail in the File S1 (page 12–29). Briefly, the tumor detector was developed using the Inception v3 classification CNN [29
]. The train subset of 129 WSIs from 65 patients contained 881,418 tiles, whereas the tune subset of 56 WSIs from 28 patients contained 332,211 tiles. A tile was classified as a tumor tile if its center position was inside the manual tumor annotations performed in the WSI; otherwise, it was classified as a non-tumor tile. This resulted in 241,170 (27%) tumor tiles and 640,248 (73%) non-tumor tiles in the train subset, and 97,587 (29%) tumor tiles and 234,624 (71%) non-tumor tiles in the tune subset. In order to represent cases with technical failures, 10 of the 185 WSIs were IHC-stained with lower PTEN antibody concentration (1:1200), and another 10 were IHC-stained without PTEN antibody. Tumor areas in these 20 WSIs were not annotated in order to allow the network to learn to exclude them. The proportion of tiles correctly classified as tumor/non-tumor was 0.957 in the train subset and 0.938 in the tune subset.
The PTEN classifier was developed using the Mask R-CNN instance segmentation network [30
]. The train subset of 24 WSIs from 24 patients consisted of 2160 tiles, and the tune subset of 10 WSIs from 10 patients consisted of 900 tiles. Contours of 77,777 tumor nuclei from the 3060 tiles were manually drawn to learn the network to identify cells. Each cell was labeled as PTEN-positive (cytoplasmic and/or nuclear staining present) or PTEN-negative (cytoplasmic and nuclear staining absent) by a trained cell biologist (KC). The train subset consisted of 46,434 (81%) PTEN-positive and 11,146 (19%) PTEN-negative cells, whereas the tune subset consisted of 17,396 (86%) PTEN-positive and 2801 (14%) PTEN-negative cells.
The development of the PTEN classifier was an iterative process. First, a model was trained and tuned using the 3060 manually annotated tiles from the 34 WSIs, resulting in a mean average precision [31
] of 0.856 in the train subset and 0.687 in the tune subset (File S1 page 19–20). To improve the model’s ability to discriminate tumor and non-tumor cells, we applied the initial model to the 3060 tiles, and the detections that did not overlap with the manual annotations were reclassified (KC) into four classes: tumor PTEN-positive, tumor PTEN-negative, non-tumor PTEN-positive, or non-tumor PTEN-negative. This refinement of the annotations in the tiles from the 34 WSIs allowed for the inclusion of tumor cells that were missed during the initial manual annotation. In addition, this allowed for the inclusion of non-tumor PTEN-positive and PTEN-negative cells that were not annotated manually but rather incorrectly identified as tumor cells by the first model to improve the network’s ability to differentiate between tumor and non-tumor cells. The tiles from the 34 WSIs were coupled with the updated annotations, including the four classes and a background class, and used to train a second (and final) model. The mean average precision of this final model was 0.835 in the train subset and 0.705 in the tune subset (File S1 page 22–23).
2.7. Statistical Analyses
The study was performed in compliance with the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) [32
]. A study protocol describing the independent validation was predefined in accordance with the Protocol Items for External Cohort Evaluation of a deep learning System (PIECES) [33
]. The primary and secondary analyses were planned prior to the evaluations of the independent validation cohort and are described in the protocol (File S1 page 35–37). The primary analysis was the assessment of the prognostic value of the automatically assessed dichotomous biomarker of PTEN status in the validation cohort by computing its hazard ratio (HR) with a 95% confidence interval (CI) in univariable Cox proportional hazard regression analysis and the p
-value of the Mantel–Cox log-rank test. The endpoint was biochemical recurrence (BCR), defined as a single PSA ≥ 0.4 ng/mL. Time to BCR (TTBCR) was calculated from primary surgery to BCR or to the date of the final PSA registration (24 June 2020). In the analysis of the test subset of the discovery cohort, the endpoint was time to recurrence defined in accordance with Punt et al. [34
], calculated from primary surgery to recurrence or to non-related death or the last date of follow-up (31 December 2008). Survival curves were depicted with the Kaplan–Meier method and compared using the Mantel–Cox log-rank test. The marker of interest and established prognostic markers were included in the multivariable model and evaluated using the Wald χ2
test with the Cox proportional hazards model. The CI of the area under the receiver operating characteristic curve (AUC) and Harrell’s concordance index (c-index) were computed as the bias-corrected and accelerated (BCa) percentile interval over 10,000 bootstraps. PTEN and DNA ploidy status were integrated with the Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) score by adding 1 point if PTEN-low and 1 point if non-diploid. In order to test the difference in c-index between the standard and the updated CAPRA-S score, a two-sided p
-value was calculated as 1 minus the confidence level of the largest BCa CI that did not contain 0. Correlations between the automatic and the manual PTEN scores were evaluated using Pearson’s correlation coefficient. The AUC was used to measure the performance of the automatic PTEN scoring method, using manual PTEN scores dichotomized using the 50% threshold as the ground truth. Fisher’s exact test, Kruskal–Wallis H, and Mann–Whitney U
tests were used to evaluate associations. Two-sided p
-values <0.05 were considered statistically significant. Statistical calculations were performed using Stata/MP 16.1 (StataCorp, College Station, TX, USA).
To our knowledge, this is the first study reporting the development of a fully automated method for scoring PTEN using IHC-stained slides, and the first study of the prognostic value of PTEN status in PCa that mitigates the challenges posed by intratumor heterogeneity. The method correlated strongly with manual scoring and was applied in three tumor-containing blocks for each patient. Using a reliable validation setup with predefined analyses, we have shown that patients with automatically assessed PTEN-low had a three-fold increased risk of BCR after RP compared to those with PTEN-high. This association remained statistically significant in multivariable analysis with established prognostic markers. Furthermore, we observed improved risk stratification when PTEN status was combined with DNA ploidy status assessed with another machine learning-based method.
We have shown that machine learning-based methods can automatically detect and quantify individual PTEN-positive and PTEN-negative tumor cells, providing a robust and accurate assessment of PTEN score. While it is difficult to explain how many modern machine learning approaches obtain their predictions [35
], the proposed approach for assessment of PTEN score is inherently more easily explained, as it is directly analogous to manual scoring. The basis of the automatic PTEN scores can be easily verified since the method provides a visual presentation of the tumor tiles as well as a localization and classification of the detected cells. Our approach could be used to develop scoring methods for scoring of other biomarkers in IHC-stained slides.
A recent study presented a method using CNNs to detect areas with PTEN-negative cells in tissue microarray (TMA) slides [17
]. However, the method could not be used to predict PTEN scores, as it did not detect areas with PTEN-positive cells nor the individual PTEN-negative cells. The method required fine-tuning to improve the correlation with manual annotations in the external TMA validation cohort, even though these slides were IHC-stained using the same conditions as the training cohort. In general, a challenge using TMAs is that they are not directly comparable to RP or biopsy specimens where the proportion of tumor to non-tumor tissue is more variable. As our method was developed in WSIs from RP specimens, which represent tumors better than TMAs, we consider it to be more feasible to implement in the clinical setting.
Unlike the published computer-aided PTEN scoring methods [17
], our method is fully automated; hence, it does not require any input from skilled personnel to manually annotate tumor areas or to ensure the quality of IHC-stained slides. Such requirements do not only entail time-consuming manual labor but can also introduce substantial inter- and intra-observer variation. Tumor areas in PCa WSIs need to be carefully annotated to exclude non-tumor cells, which are often intermixed with the tumor cells and may confound the PTEN score. Our method includes multiple steps to ensure that only tumor cells are used to calculate the PTEN score, both by excluding non-tumor regions as well as benign epithelial or stromal cells within the tumor regions.
Areas in which IHC staining appeared to be absent in tumor cells and weak in internal controls were the main source of discrepancies between the manual and automatic PTEN scores. Such areas were considered to represent technical failures and therefore omitted when scoring manually, whereas some were scored by the automatic method, resulting in lower automatic PTEN scores for some WSIs (Figure 2
). Our method could perhaps be further improved by using a larger set of WSIs representing true technical failures, where the presence of PTEN protein had been confirmed by other assays. However, the interpretation of staining intensity is subjective, and some of these tumor areas might have been wrongly considered as technical failures when scoring manually. Overall, the manual and automatic PTEN scores were strongly correlated and provided similar prognostic information when analyzed as continuous as well as dichotomous markers.
As far as we know, all previous studies on the prognostic value of PTEN status in RP specimens were performed using TMAs [16
] or a single tumor-containing block per patient [38
]. As PTEN protein expression displays considerable intratumor heterogeneity [24
], such sparse sampling may lead to a misclassification of PTEN status. To better represent intratumor heterogeneity in prostate tumors, we assessed PTEN status in WSIs from three different tumor-containing tissue blocks for each patient.
There is currently no consensus on how to dichotomize PTEN scores in IHC studies, and thresholds of 90% [37
], 50% [39
], and 10% [16
] PTEN-negative cells have previously been used for manual PTEN scores. The study by Jamaspishvili et al. [18
] assessed PTEN scores semi-automatically and defined the threshold in a discovery cohort and validated it in an independent validation cohort. This study was performed using TMAs, and the threshold of 65% PTEN-negative cells was selected using maximally log-rank statistics, and BCR defined as two PSA values ≥ 0.2 ng/mL as an endpoint. We selected the 50% threshold to dichotomize PTEN status because this threshold provided relatively large HRs and c-indices across several clinically relevant endpoints in the test subset and was considered to be suited for contemporary cohorts where fewer patients have advanced disease at the time of surgery compared to those in the test subset (File S1 page 33–34).
The automatic PTEN scoring method was validated in the independent cohort using BCR as the endpoint, which is a limitation of our study. BCR is an intermediate endpoint, which does not always translate into clinical recurrence or PCa death [40
]. However, we defined BCR as a single PSA ≥ 0.4 ng/mL, which is suggested to exclude most patients with detectable PSA who are unlikely to progress [41
]. We observed larger HR and c-indices of PTEN status in the validation cohort than in the test subset. This could be due to the use of BCR as an endpoint and the fact that tumors in the test subset were more advanced compared to those in the validation cohort, and PTEN status is suggested to provide stronger prognostic information in patients with less advanced tumors [15
The combination of automatically assessed PTEN and DNA ploidy status provided stronger prognostic information than either marker alone when comparing the HRs and c-indices. Patients with both PTEN-low and non-diploid tumors had a 4.63 times increased risk of BCR compared to those with PTEN-high and diploid tumors, suggesting that these two alterations together may result in a more aggressive tumor phenotype. However, the addition of the combined marker to CAPRA-S score did not provide a significant increase in prognostic discrimination in terms of the c-index. The CAPRA-S score includes GS and factors used to determine tumor stage, which are strong prognostic parameters in the postoperative setting [22
], but their assessment is subjective and is best when performed by experts [45
]. Importantly, our cohorts comprised patients operated on at the tertiary comprehensive cancer center, where the routine pathological examination of RP specimens is likely better than in the community hospitals [46
], and central review of GSs was performed by a highly experienced uropathologist.
Automatic measurements of PTEN and DNA ploidy status may be particularly useful in the preoperative setting, where the complete GS and tumor staging information is not available. Therefore, prediction of patient outcomes by pathological assessment is less accurate in the preoperative setting compared to the postoperative setting [47
]. Assessment of cribriform morphology and/or intraductal carcinoma on diagnostic biopsies is suggested to refine the current GGGs and aid in selection of patients for active surveillance [50
]. These morphological characteristics were shown to be associated with increased genomic instability and PTEN loss [51
], but still their assessment suffers from inter-observer variation [53
]. A limitation of our PTEN scoring method is that it was developed in RP specimens and, thus, it may not be directly applicable for use on biopsies, where tumor areas are smaller. On the other hand, RP specimens provide a large amount of data, which is beneficial when training CNNs and which would be challenging to obtain from biopsies. However, we hypothesize that our PTEN scoring method could be optimized for use in a biopsy setting by applying transfer learning [55
] and a small discovery dataset of biopsy samples.