Early detection of cancer often leads to better survival (1), because pre-malignant lesions and 24 early stage tumors can be more effectively treated (2). Most pre-malignant lesions amenable to 25 early detection rely on targeted sampling and show only minor tissue changes on pathology as-26 sessment (3-5). In addition, pathology procedures often involve laborious and time-consuming 27 steps which can lead to errors and adversely affect patient care (6). Recent developments in Ar- 28 tificial Intelligence (AI) have achieved excellent performance on diagnostic tasks (7-9). How-29 ever, understanding how these techniques can be integrated into clinical workflows most effi-30 ciently and to assess the actual benefits they bring remains a challenge. The design of a clinical 31 decision support system needs to balance its performance against workload reduction and po-32 tential economic impact. Replacing pathologists entirely could lead to substantial workload 33 reduction, but such an approach would only be viable if performance remains comparable to 34 that of human experts. Between a fully automated approach and the status quo of fully manual 8 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. Next, patient-level counts were compared to endoscopy ground truth for detecting BE on 149 the internal validation set (Methods). This ground truth was defined according to the Prague 150 criteria (Methods) with confirmed IM on endoscopy biopsies (30). To calculate sensitivity and 151 specificity for the fully automated method on the internal validation cohort, we used operating 152 points determined on the calibration cohort (Table S2) CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint We then explored whether a different modelling approach based on established decision path-164 ways could boost performance. We developed a triage-driven, semi-automated approach as an 165 alternative to the fully automated approach described above. Both approaches use the same 166 patient-level aggregations as input, but their outputs are different: the fully automated approach 167 tries to directly mimic pathology assessment by classifying patients as positive or negative for 168 BE. In contrast, the triage approach defines different quality and diagnostic confidence classes 169 to select challenging patient samples for manual review. Although it cannot reduce workload as 170 much as a fully automated approach, a triage approach keeps sample stratification more inter-171 pretable and transparent. 172 We first selected deep learning architectures and defined cut-offs for different quality and gastric-type columnar epithelium on H&E were classified as no confidence, low confidence, or 178 high confidence ( Figure S6a , Table S4 ). For diagnostic confidence classes, the number of tiles 179 detected with TFF3-positive goblet cells were classified as high confidence negative, low confi-180 dence equivocal, or high confidence positive ( Figure S6b , Table S4 ). On the internal validation 181 cohort, we observed a visual agreement between these confidence classes and pathology and 182 endoscopy ground truths ( Figure 3 , Table S5 ). 183 We then combined the quality and diagnostic classes into eight triage classes of varying 184 priority for manual review (Figure 4a ). The relative priority of each class was determined by 185 expert pathologists: Cases with low confidence in sample quality (none or few columnar ep-186 ithelium detected on H&E) or low confidence in diagnosis (few goblet cells detected on TFF3) 187 
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The copyright holder for this preprint this version posted July 17, 2020. Figure 3 : Application of quality control and diagnostic confidence class scheme to internal validation cohort. a Quality ground truth by pathologist from Cytosponge (top) compared with number of detected columnar epithelium (CE) tiles on H&E detected by VGG-16 (bottom). b Diagnosis ground truth by pathologist from Cytosponge (top), Endoscopy (with confirmed IM on biopsy) ground truth (middle) compared with number of detected TFF3-positive tiles on TFF3 detected by ResNet-18 (bottom) / eqv. = equivocal.
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The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint negative evidence. In our internal validation cohort, we find that only 13.0% of patients fall 189 into the triage classes with high priority (4 and 5), while 87.0% fall into the other six classes 190 (Figure 4a ). 191 We next asked which classes can be substituted by automated review while retaining the 192 accuracy of full manual review by a human pathologist (sensitivity: 81.7%; specificity: 92.7%). 193 We applied a cumulative substitution scheme and started by substituting class 1 with automated 194 review, then classes 1 and 2, then classes 1, 2, and 3, and so on. In the validation cohort, we 195 found that sensitivity and specificity remain stable if classes 1, 2, and 3 are substituted, but Our case-control cohort is not representative of a real-world population eligible for Cytosponge-208 TFF3 testing. In our internal validation set we had a disease prevalence of 50.0%, while the 209 prevalence expected in a real-world population with GERD symptoms ranges from 3.0% to 210 7.5% (17, 31-33). Additionally, the allocation of samples to triage classes depends directly on 211 12 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 17, 2020. Figure 4 : Triage-driven approach with incremental triage class substitution scheme on internal validation set a Table of quality control and diagnosis classes. Each class has been assigned a qualitative priority for manual review. Column 'Automated result' refers to the label a sample would be assigned if all samples of this class were automatically reviewed. Asterisk (*): includes combination of no confidence (quality control) and high confidence positive (diagnosis) despite minimal likelihood of occurrence. b Cumulative substitution scheme starting with fully manual review, followed by substitution with automated review of class no. 1, then 1 and 2, etc. Red rectangle indicates a drop of performance at substitution stage. c Cumulative substitution scheme starting with fully manual review, followed by substitution with automated review of class no. 8, then 8 and 7, etc. Red rectangle indicates a drop of performance at substitution stage.
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The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint the amount of sampled cellular material and the resulting sample confidence, which can vary 212 widely and might improve with future refinements of the device administration procedure.
To understand how our results generalize, we devised a simulation approach to vary how (determined by quality control and diagnostic class: 2 and 8) and lower sample confidence (1, 221 3, 4, 5, 6, and 7). We then modified the proportion of high confidence samples and inversely 222 adapted the proportion of lower confidence samples within a range from -25% to 25%.
Over a fine grid of varying disease prevalence and changes in sample confidence, we ob-224 served a negative impact of decreasing cohort BE prevalence and a positive impact of sample 225 confidence on the potential workload reduction (Figure 5a ). According to this simulation, in 226 a realistic cohort with a BE prevalence of 7%, we would still be able to reduce the pathology 227 workload by 57%. In order to retain the same workload reduction we observed in the validation 228 cohort, the proportion of samples with high confidence in a realistic cohort would need to be 229 increased by 15%. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 17, 2020. Li ne of eq ua l w or kl oa d re du ct io n Figure 5 : Triage model applied to external validation cohort and simulation of cohort variation Simulation of changes in cohort prevalence of BE and sample confidence with impact on workload reduction. Every contour line (blue) represents the same level of workload reduction as indicated by the percentages. Solid black lines indicate the workload reduction of the validation cohort. The dotted yellow line illustrates the workload reduction of a realistic primary care referral cohort (with 7% prevalence) with no change in sample confidence classes (lower yellow marker) and the confidence change required to match the same amount of workload reduction as in the validation cohort (upper yellow marker). The results from the external validation cohort are shown in red.
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The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint described in the previous section, we used manual pathologist reviews for samples that fell into 236 triage classes 4, 5 and 6. In the BEST3 trial, endoscopy data was only available for positive 237 Cytosponge patients and those who had Barrett's diagnosed at follow-up as a result of standard 238 of care. In addition, the trial was not designed to investigate sensitivity or specificity but positive 239 predictive value (PPV) instead. We also calculated the negative predictive value (NPV) based CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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We have presented a triage-driven approach that analyses samples of the Cytosponge-TFF3 test 254 using deep learning for the early detection of esophageal cancer. Our approach combines quality 255 control and diagnostic metrics of pathology slides to stratify patients into 8 triage classes which 256 determine whether a patient sample requires manual or if automated review would suffice.
For the analysis of Cytosponge-TFF3 samples, our triaging approach has several benefits: 258 We are able to substantially reduce workload and match the sensitivity and specificity of expert 259 pathologists. In our internal validation cohort, fully manual review by a pathologist achieves 260 81.7% sensitivity and 92.7% specificity. In a fully automated approach, we observed a sensitiv-261 ity of 72.6% and a specificity of 93.1%. With our triage-driven approach, we demonstrate that 262 up to 66% of cases can be reviewed automatically while achieving a sensitivity of 82.5% and 263 specificity of 92.7%, a performance marginally superior to fully manual review by pathologists. In this more realistic cohort, 57.41% workload for the pathologists would have been reduced.
These results ( Figure 5 ) have several implications: First, a fully automated review would re-270 duce sensitivity (at fixed specificity) and therefore suffer from a loss of clinical utility. Second, 271 while a triage-driven approach is not able to reduce workload as much as a fully automated ap-272 proach, the described triage classes provide a logical way for stage-wise clinical adoption and 273 performance testing in routine practice.
Another benefit of our approach is that we were able to directly adopt heuristics applied 275 by pathologists familiar with Cytosponge-TFF3 samples in our algorithmic design process. As 276 17 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint a result, our approach demonstrates traceability and interpretability (8): First, we mimicked 277 the screening process of samples observed by expert pathologists by replicating their decision-278 making scheme (Figure 1c) . Second, the saliency maps we generated from deep learning mod-279 els to visualize learned features in the pathology images show strong agreement with manual 280 landmarks placed by pathologists ( Figure S2 ).
As a further benefit, our triage approach achieves strong performance from only 287 sam- is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint assurance. In future work, we plan to test whether the superiority of the triage-driven approach 303 over fully manual pathologist review will generalize by incorporating multi-centre data from 304 ongoing and future Cytosponge-TFF3 studies to evaluate this effect more extensively. 305 Second, the underlying machine learning model could be further optimized. For example, 306 instead of using a transfer learning model based on pre-training with a primary dataset, we could 307 train a model from scratch, which has proven to improve results in some CNN applications (37).
In addition, the tile size needs further investigation because it determines the receptive field in In summary, our triage approach differs from previous applications of deep learning to medi-324 cal images (7,34) which used fully automated approaches on extremely large datasets. We show 325 that for a modest dataset size, leveraging existing heuristics of pathologist decision-making in a 326
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The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint generalises well to an independent validation cohort. These results lay the foundation for tai-328 lored, semi-automated decision support systems embedded in clinical workflows. 329 20 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. All slides were annotated using the existing patient-level ground truth data for comparison. We 408 aimed for a representative fraction of available material on each slide to be labelled. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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In order to compare the performance of all six deep learning architectures, we calculated class-450 specific performance in the quality control and diagnosis frameworks (Table S1 ). To obtain 451 these numbers, we selected the epochs with the best weighted accuracy score on the develop- is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. . We generated saliency maps for both models trained on H&E and TFF3, respectively. The . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint this version posted July 17, 2020. (Table S2) . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint this version posted July 17, 2020. (Table S5) .
The two operating points for quality control and diagnosis resulted in three tiers per frame- CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 17, 2020. . https://doi.org/10.1101/2020.07.16.20154732 doi: medRxiv preprint automated review were calculated and compared against the previous substitution steps. The 589 substitution scheme was applied starting from both ends of the triage class list. First, class 1 590 was substituted with automated review, then classes 1 and 2, then classes 1, 2, and 3, and so on. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The copyright holder for this preprint this version posted July 17, 2020. 1, 2, 3  1, 2, 3, 4  1, 2, 3, 4, 5  1, 2, 3, 4, 5, 6  1, 2, 3, 4, 5, 6, 7  1, 2, 3, 4, 5, 6, 7 Figure S7 : Performance of semi-automated, triage-driven model on external validation cohort a Cumulative substitution scheme starting with fully manual review, followed by substitution with automated review of class no. 1, then 1 and 2, etc. b Cumulative substitution scheme starting with fully manual review, followed by substitution with automated review of class no. 8, then 8 and 7, etc.
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The copyright holder for this preprint this version posted July 17, 2020. Table S1 : Tile-level precision and recall for all classes from H&E and TFF3 models. This data is derived from the tiles in the development set. (DenseNet = DenseNet-121, Inception = Inception v3, ResNet = ResNet-18, VGG = VGG-16). The highest value(s) per row is/are highlighted in bold.
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The copyright holder for this preprint this version posted July 17, 2020. Table S2 : Individual probability threshold calibration with associated performance based on differential ROC analysis for quality control and diagnosis. The AUC for quality control relates to the performance on the calibration cohort at the given probability threshold for individual tiles containing columnar epithelium on H&E. The AUC for diagnosis relates to the performance on the calibration cohort at the given probability threshold for individual tiles containing positive goblet cells on TFF3. Sensitivity is based on a fixed value of specificity derived from the pathologist performance on the calibration cohort. The tile number threshold is the resulting cutoff from the fixed specificity.
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The copyright holder for this preprint this version posted July 17, 2020. . Table S3 : Performance of all architectures after application on the validation cohort. Quality control models relied on pathologist calls on sample quality. Sensitivities or specificities were not determined due to irrelevance in the fully automated model approach. Diagnosis models relied on thresholds determined on the calibration cohort.
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The copyright holder for this preprint this version posted July 17, 2020. Table S4 : Characteristics of patients in quality control and diagnosis classes from calibration cohort. For each of the three quality control and diagnosis classes, the number of patients within the class and the paired ground truth is shown. Table S5 : Characteristics of patients in quality control and diagnosis classes from validation cohort. For each of the three quality control and diagnosis classes, the number of patients within the class and the paired ground truth is shown.
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The copyright holder for this preprint this version posted July 17, 2020. Table S6 : Characteristics of patients in quality control and diagnosis classes from external validation cohort. For each of the three quality control and diagnosis classes, the number of patients within the class and the paired ground truth is shown.
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