Histopathological growth patterns of colorectal liver metastasis exhibit little heterogeneity and can be determined with a high diagnostic accuracy

The patient selection for the current study was performed in the same cohort as described by Galjart et al. [15]. Patients undergoing resection of CRLM at the Erasmus MC Cancer Institute, the Netherlands, between January 2000 and March 2015 were eligible for inclusion.

During macroscopic pathological assessment of the surgical specimens of resected CRLM, representative sections (e.g. tumour, tumour with relation to the surgical margin(s), capsule, background liver, non-tumorous liver in distance) were considered for preparation of FFPE tissue blocks. A 5 µm section per block was cut and stained with Haematoxylin and Eosin (H&E) for pathological interpretation. If needed, deeper levels of the block were cut and stained with H&E.

H&E stained slides retrieved from the archive of the Pathology Department of the Erasmus MC were retrospectively reviewed by light microscopy (Fig. 1a–f). Scoring of the HGPs was performed in accordance with international consensus guidelines [14]. For each block subjected to review the relative presence [in percentage (%)] at the tumour-liver interface of the distinct HGP’s (pushing, desmoplastic and replacement type) was estimated. The metastasis HGP was defined as the pooled estimate (average with equal weights per block) of all blocks of a single CRLM. Concordantly, the patient HGP was defined as the pooled estimate (average with equal weights per CRLM) of all resected CRLM within a single patient. Given recent findings [15], block, metastasis and patient HGP were classified as dHGP if only the desmoplastic type was observed (i.e. 100% dHGP), and as non-dHGP if any percentage of pushing and/or replacement type was observed (i.e. < 100% dHGP). Due to this on/off classification, if non-dHGP is observed on a single block, corresponding metastasis and patient HGP is classified as non-dHGP, regardless of the HGP of other blocks within the same metastasis or other CRLM within the same patient.

For the within metastasis analysis, concordance (yes/no) of block HGP to metastasis HGP was recorded for all resected CRLM with ≥ 2 tissue blocks. Within metastasis concordance was defined as the proportion of concordant tissue blocks. Since a lesion represents a three dimensional structure, consecutive slides from a single block (i.e. deeper levels) do not adequately represent its three dimensional nature. As such, consecutive slides from a single block were excluded from the within metastasis analysis. For the between metastasis analysis, concordance (yes/no) of metastasis HGP to patient HGP was determined in all patients with ≥ 2 CRLM resected in a single time-frame (e.g. no recurrent CRLM). Between metastasis concordance was defined as within patient proportion of concordant CRLM. Patient information and data on primary CRC and CRLM were extracted from a prospectively maintained database. Regarding systemic treatment status, patients were considered chemo-naive if they did not receive any form of chemotherapy within the 6 months prior to resection. Multivariable logistic regression analysis was performed for within metastasis discordance (yes/no) with primary tumour characteristics, known clinical risk factors, systemic treatment status and number of blocks scored entered into the model. Significant predictor(s) found for within metastasis discordance were used as stratification factor(s) for between metastasis analysis. Identical models were fitted within each stratum (if applicable) to predict discordance (yes/no) amongst multiple metastases. Mean within metastasis concordance was compared across number of blocks scored. Similarly, mean between metastasis concordance was compared within strata (if applicable) and by number of CRLM resected.

External validation of mean within and mean between metastasis concordance was performed by retrospective HGP assessment as described previously. The external validation cohort comprised of chemo-naive patients treated surgically for CRLM at the University Hospital of Heidelberg, Germany, between October 2001 and June 2009 [24]. H&E stained sections of the validation cohort were provided by the tissue bank of the National Center for Tumor Diseases (NCT). As the external validation cohort consisted of chemo-naive patients, comparisons to the original cohort were performed in (tissues from) chemo-naive patients only.

Diagnostic accuracy for scoring a single FFPE block was determined in all CRLM with ≥ 2 blocks. Of these ≥ 2 blocks, one individual block was selected at random. The HGP of this randomly selected block was considered the predictor (i.e. test result), while the metastasis HGP—as determined by HGP assessment of all ≥ 2 blocks of the metastasis in question—was considered the response (i.e. true HGP status). This was done similarly for 2 blocks in all CRLM with ≥ 3 blocks. Identically, the diagnostic accuracy of scoring a single resected CRLM was determined within patients with ≥ 2 CRLM resected etc. The area under the curve [AUC] of the corresponding receiver operating characteristic (ROC) curves were compared for 2 versus 1 block(s) or CRLM scored, and for 3 versus 2 blocks or CRLM scored, respectively.

A gastro-intestinal pathologist (MD) and a PhD-candidate (DH) without prior pathology experience were recruited for learning curve analysis. Both observers had no prior experience in HGP assessment. The raters received a joint training session by a pathologist with over 10 years of experience in HGP assessment (PV). During this training session, 50 tissue sections were assessed collaboratively. Hereafter, both observers independently scored a test-set of an additional 50 tissue sections. Individual scores of the test-set were reviewed in a joint session with the trainer, followed by a second training session of 50 tissue sections. Subsequently a second test-set of 50 tissue sections was scored independently. After completion scores were again collaboratively reviewed. For both test-sets, interobserver agreement of both observers compared to the gold standard was determined for the dHGP/non-dHGP classification. The scores of the experienced trainer were considered the gold standard.

Dichotomous or categorical data are reported as percentage, parametric continuous data are reported as mean (standard deviation [SD]) and non-parametric continuous data are reported as median (inter-quartile range [IQR]). Mean concordances were compared by an independent samples T test or a one-way analysis of variance (ANOVA), depending on the number of strata. AUC values were compared as described by DeLong [26]. Interobserver agreement was determined using Cohen’s kappa. All statistical analyses were performed using R version 3.5.3 (http://​www.​r-project.​org). The R-package ‘pROC’ was used for comparison of AUC values. A p-value < 0.05 was considered statistically significant.

In total 785 patients underwent resection of one or more CRLM at the Erasmus MC Cancer Institute in the study period and were consequently scored for HGP. In total 1625 CRLM were resected. Of these, 835 CRLM had two or more H&E stained slides available for review (2135 slides in total) and were considered for within metastasis analysis. Of these, 31 slides of ten individual CRLM were identified as consecutively cut from single FFPE blocks, and were hence excluded from within metastasis analysis. Resection of two or more CRLM was performed in 382 patients. Nineteen were excluded for between metastasis analysis due to missing data required to link individual tissue samples to individual CRLM. Within the remaining 363 patients a total of 1118 CRLM were resected. Patient characteristics are reported in Table 1.

Non-dHGP was observed in 72% of reviewed tissue blocks. Results of the multivariable logistic regression model on within metastasis discordance are reported in Table 2. Systemic treatment status proved to be a significant predictor for presence of HGP discordance (yes/no) amongst multiple blocks, with an odds ratio (OR) (95% CI) of 2.107 (1.231; 3.679) and p = 0.007 for pre-treated versus chemo-naive CRLM. Mean within metastasis concordance was 95%. Figure 2a shows the mean within metastasis concordance stratified by number of blocks scored. Mean within metastasis concordance (95% CI) for 2, 3, 4, or ≥ 5 blocks scored was 96% (95; 97), 94% (92; 96), 93% (88; 98) and 94% (86; 100) respectively and was independent of the number of blocks scored (p = 0.315).

Mean between metastasis concordance of all 363 patients was 90%. Since systemic treatment status was a significant predictor for within metastasis discordance, between metastasis analysis was performed in chemo-naive and pre-treated patients separately. Non-dHGP was found in 85% of chemo-naive patients versus 78% in pre-treated patients (p = 0.094). Results of the fitted multivariable logistic regression models on presence of HGP discordance (yes/no) amongst multiple resected CRLM are reported in Table 2. Within chemo-naive patients, the size of the largest hepatic tumour on preoperative imaging proved a significant predictor for between metastasis discordance with OR (95% CI) 1.461 (1.073; 2.145) and p = 0.028 for every cm increase in size. The only significant predictor found for between metastasis discordance in pre-treated patients was number of CRLM resected. Corresponding OR (95% CI) were 3.602 (1.414–9.550) for 3 versus 2 CRLM resected and 5.887 (2.585; 14.356) for ≥ 4 versus 2 CRLM resected (p = 0.008 and p < 0.001). Mean between metastasis concordance (Fig. 2b) was significantly lower in pre-treated versus chemo-naive patients (88% vs. 94%, p = 0.006). Figure 2b shows the mean between metastasis concordance for chemo-naive and pre-treated patients stratified by number of CRLM resected. In chemo-naive patients, mean between metastasis concordance [95% CI] did not differ amongst 2 (94% [91; 98]), 3 (94% [88; 99]) or ≥ 4 (90% [78; 100]) CRLM resected (p = 0.678). In pre-treated patients mean between metastasis concordance [95% CI] was significantly different amongst 2 (93% [90; 96]), 3 (85% [78; 92]) and ≥ 4 (83% [77; 88]) CRLM resected (p = 0.004).

The external cohort comprised of 276 patients of whom the HGP could be determined in 251 (91%). In total 168 patients had resection performed of two or more CRLM and could be included for between metastasis analysis. Within metastasis analysis was performed in 270 CRLM with two or more blocks. Baseline characteristics were comparable between the external validation cohort and chemo-naive patients within the original cohort (Supplementary Table 1). Mean within (96% vs. 97%, p = 0.652) and between (94% vs. 94%, p = 0.710) metastasis concordance did not differ between the original (chemo-naive patients only) and validation cohort (Fig. 3).

Supplementary Fig. 1a displays the AUC for scoring a single (95.9%), two (97.7%) or three blocks (98.8%) per metastasis. A significant increase in diagnostic accuracy was observed for scoring 2 versus 1 block(s) (p = 0.039), but not for scoring 3 versus 2 blocks (p = 0.341). The AUC for scoring a single (93.3%), two (96.5%) or three (98.2%) resected CRLM per patient are reported in Supplementary Fig. 1b. A significant increase in diagnostic accuracy was found for scoring 2 versus 1 resected CRLM (p = 0.026), but not for scoring 3 versus 2 resected CRLM (p = 0.235).

The results of both test-sets as scored by the gold standard, the pathologist and the PhD candidate are graphically displayed in Fig. 4a–f. Interobserver agreement was higher in the second test-set for both the pathologist (k = 0.953 vs. k = 0.836) and the PhD candidate (k = 0.951 vs. k = 0.747). Where in the first test-set a difference in performance was seen between the pathologist and the PhD candidate (k = 0.836 and k = 0.747), performance in the second test-set did not differ (k = 0.953 and k = 0.951).