Correction to Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumours and differentiation from small intestinal neuroendocrine tumours. J Neuroendocrinol. 2022 Jul;34(7):e13176.

Espen Thiis-Evensen¹, Magnus Kjellman², Ulrich Knigge³, Henning Gronbaek⁴, Camilla Schalin-Jäntti⁵, Staffan Welin⁶, Halfdan Sorbye⁷, Maria del Pilar Schneider⁸ and Roger Belusa⁹ on behalf of The Nordic NET Biomarker Group

Due to the use of a wrong version of the informed consent form four patients have to be excluded from the analyses. This has unfortunately led to other results than previously published. We sincerely apologize for this. In paragraph 2 of the “Abstract” section, the text “Methods: At time of diagnosis blood samples were collected and analysed from 34 patients with PanNET, 135 with SI-NET, (WHO Grade 1-2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Proseek Oncology-II (OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A (CgA) was analysed separately.” was incorrect.

This should have read: “Methods: At time of diagnosis blood samples were collected and analysed from 34 patients with PanNET, 135 with SI-NET, (WHO Grade 1-2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Proseek Oncology-II (OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A (CgA) was analysed separately.”

In paragraph 3 of the “Abstract” section, the text “Results: Median age in all groups was 65-67 years and with a similar gender distribution (Female; PanNET 51%, SI-NET 42%, controls 42%). Tumour grade (G1/G2): PanNET 39/61%, SI-NET 46/54%. Patients with liver metastases: PanNET 78%, SI-NET 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.84, specificity (SPE) 0., positive predictive value (PPV) of 0.92 and negative predictive value (NPV) of 0.95, and area under ROC (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.61, SPE 0.9, PPV 0.83, NPV 0.90 and AUROC 0.98).” was incorrect.

This should have read: “Results: Median age in all groups was 65-67 years and with a similar gender distribution (Female; PanNET 53%, SI-NET 42%, controls 42%). Tumour grade (G1/G2): PanNET 35/53%, SI-NET 46/54%. Patients with liver metastases: PanNET 71%, SI-NET 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.71, specificity (SPE) 0.98, positive predictive value (PPV) of 0.91 and negative predictive value (NPV) of 0.94, and area under ROC (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.48, SPE 0.96, PPV 0.76, NPV 0.88 and AUROC 0.90).”

In line 116 of the “Materials and Methods” section, the text “A total of 135 patients with SI-NETs and 39 with PanNET were included in the study.” was incorrect. This should have read: “A total of 135 patients with SI-NETs and 34 with PanNET were included in the study.”

In paragraph 1 of the “Results” section, the text “Baseline characteristics of the three study groups are given in Table 1. Baseline chromogranin A (nmol/L) values as mean (SD) and median (range) were for controls: 4.4 (4.8) and 3.3 (2-43), for SI-NET: 46.7 (85.5) and 12.0 (2-620) and for PanNET: 55.8 (145.4) and 8.8 (2-785).” was incorrect.

This should have read: “Baseline characteristics of the three study groups are given in Table 1. Baseline chromogranin A (nmol/L) values as mean (SD) and median (range) were for controls: 4.4 (4.8) and 3.3 (2-43), for SI-NET: 46.7 (85.5) and 12.0 (2-620) and for PanNET: 46.6 (135.7) and 7.9 (2-785).”

In paragraph 2 of the “Results” section, the text “Altogether 33 (24%) patients with SI-NET and 5 (13%) patients with PanNET had primary tumour surgery prior to study inclusion. At the time of diagnosis (baseline visit) 78% of the SI-NET and 57% of the PanNET cohort had CgA levels >ULN. Eighteen (46%) patients with PanNET had less than three liver metastases and nine (23%) patients had no liver metastases. In this non-functional PanNET patient cohort, only a minority (15%) of patients had any type of daily symptoms that could be related to the NET. In the SI-NET cohort, 48% of the patients had at least one daily symptom that could be tumour related, such as flushing, diarrhoea or abdominal pain.” was incorrect.

This should have read: “Altogether 33 (24%) patients with SI-NET and 5 (15%) patients with PanNET had primary tumour surgery prior to study inclusion. At the time of diagnosis (baseline visit) 78% of the SI-NET and 59% of the PanNET cohort had CgA levels >ULN. Fifteen (44%) patients with PanNET had less than three liver metastases and 10 (29%) patients had no liver metastases. In this non-functional PanNET patient cohort, only a minority (21%) of patients had any type of daily symptoms that could be related to the NET. In the SI-NET cohort, 48% of the patients had at least one daily symptom that could be tumour related, such as flushing, diarrhoea or abdominal pain.”

In paragraph 3 of the “Results” section, the text “Results from model 1 (PanNET vs controls) performance metrics are presented in Table 2. BT and SVM yielded very similar results in terms of accuracy (ACC) performance, for both models including the 92 plasma proteins and CgA (ACC models 1A (mean (SD)), BT=0.91 (0.04); SVM=0.94 (0.01)) or after excluding CgA (ACC models 1B, BT=0.91 (0.03); SVM=0.94 (0.01)). LDA showed lower predictive performance (ACC=0.82 (0.06) for both model 1A and 1B. Mean misclassification rate of the classifiers ranged from 6% to 18%.” was incorrect.

This should have read: “Results from model 1 (PanNET vs controls) performance metrics are presented in Table 2. BT and SVM yielded similar results in terms of accuracy (ACC) performance, for both models including the 92 plasma proteins and CgA (ACC models 1A (mean (SD)), BT=0.90 (0.05); SVM=0.92 (0.02)) or after excluding CgA (ACC models 1B, BT=0.89 (0.04); SVM=0.92 (0.02)). LDA showed lower predictive performance (ACC=0.83 (0.05 and 0.07) for both model 1A and 1B, respectively. Mean misclassification rate of the classifiers ranged from 8% to 17%.”

In paragraph 4 of the “Results” section, the text “The best accuracy performance to classify between PanNET vs SI-NET (Model 2, Table 3) was achieved with SVM, thus, for both models including the 92 plasma proteins and CgA (model 2A, ACC=0.91 (0.07)) or excluding CgA (model 2B, ACC=0.93 (0.05)). Similar accuracy was yielded for both BT and LDA models including the 92 plasma proteins and CgA (ACC models 2A, BT=0.84 (0.03); LDA=0.83 (0.05)) or excluding CgA (ACC models 2B, BT=0.83 (0.02); LDA=0.83 (0.04)). Mean misclassification rate of the classifiers ranged from 7% to 17%.” was incorrect.

This should have read: “The best accuracy performance to classify between PanNET vs SI-NET (Model 2, Table 3) was achieved with SVM, thus, for both models including the 92 plasma proteins and CgA (model 2A, ACC=0.92 (0.04)) or excluding CgA (model 2B, ACC=0.89 (0.04)). A somewhat higher accuracy was yielded for BT compared to LDA models including the 92 plasma proteins and CgA (ACC models 2A, BT=0.86 (0.02); LDA=0.75 (0.05)) or excluding CgA (ACC models 2B, BT=0.85 (0.03); LDA=0.76 (0.05)). Mean misclassification rate of the classifiers ranged from 8% to 25%.”

In paragraph 5 of the “Results” section, the text “The best BT model for classifying PanNET vs. controls was obtained when CgA was excluded from the model and only the top biomarkers (Table 2) were used: SEN= 0.84 (0.15), SPE= 0.98 (0.15), PPV= 0.92 (0.07), NPV= 0.95 (0.05) (Table 2). Figure 1 shows performance metrics box-plots for BT model 1B (best model) including top biomarkers (plasma proteins without CgA).” was incorrect.

This should have read: “The best BT model for classifying PanNET vs. controls was obtained when CgA was excluded from the model and only the top biomarkers (Table 2) were used: SEN= 0.71 (0.18), SPE= 0.98 (0.02), PPV= 0.91 (0.09), NPV= 0.94 (0.04) (Table 2). Figure 1 shows performance metrics box-plots for BT model 1B (best model) including top biomarkers (plasma proteins without CgA).”

In paragraph 7 of the “Results” section, the text “When discriminating between PanNET and SI-NET populations (Model 2), more variable results and somewhat lower performance metrics were observed compared to detecting PanNET versus controls (Table 3 and Figure 2). This was found both when all 92 plasma proteins were included in the models with CgA (Model 2A) or when CgA was excluded (Model 2B). Better predictive performance was obtained when only the top biomarkers (Table 3) were included in the BT model, SEN= 0.61 (0.03), SPE= 0.96 (0.02), PPV= 0.83 (0.11), NPV= 0.90 (0.02) (Table 3). Figure 2 shows performance metrics box-plots for BT model 2A (best model) including top biomarkers (plasma proteins with CgA). CgA was more important for detecting SI-NET than PanNET (this study and ref. 19). Receiver operating characteristic curve and corresponding area under curve values (AUROC) generated from the BT model analysis of detecting PanNET versus SI-NET was 0.98 (0.02) and 0.97 (0.01) for top biomarker models.” was incorrect.

This should have read: “When discriminating between PanNET and SI-NET populations (Model 2), more variable results and somewhat lower performance metrics were observed compared to detecting PanNET versus controls (Table 3 and Figure 2). This was found both when all 92 plasma proteins were included in the models with CgA (Model 2A) or when CgA was excluded (Model 2B). Better predictive performance was obtained when using SVM model including CgA (Table 3), SEN= 0.68 (0.23), SPE= 0.98 (0.04), PPV= 0.92 (0.14), NPV= 0.93 (0.05) (Table 3). Figure 2 shows performance metrics box-plots for SVM model 2A (best model) including all biomarkers (plasma proteins with CgA). CgA was more important for detecting SI-NET than PanNET (this study and ref. 19). Receiver operating characteristic curve and corresponding area under curve values (AUROC) generated from the SVM model analysis of detecting PanNET versus SI-NET was 0.90 (0.05).”

In paragraph 9 of the “Results” section, the text “CgA used alone for detecting PanNET had a sensitivity of 41%, a specificity of 94%, a PPV of 64% and a NPV of 84%.” was incorrect.

This should have read: “CgA used alone for detecting PanNET had a sensitivity of 14%, a specificity of 98%, a PPV and a NPV of 083%.”

In paragraph 1 of the “Discussion” section, the text “In this study we used a proximity extension assay to investigate 92 plasma proteins known to be associated with malignancy in general. After analysing plasma, collected prior to any NET specific treatment being initiated, supervised machine learning methods were able, with high sensitivity and specificity, to discriminate patients with PanNET from controls based on the presence of specific proteins. We were also able to differentiate patients with PanNET from patients with SI-NET. Our data supports data from a previously study we recently published, using the same methodology, where the top 12 of the 92 proteins, also used in this study, differentiated SI-NET from controls.19 This indicates that findings from this multi biomarker strategy could be applied also on patients with neuroendocrine tumours from other primaries.” was incorrect.

This should have read: “In this study we used a proximity extension assay to investigate 92 plasma proteins known to be associated with malignancy in general. After analysing plasma, collected prior to any NET specific treatment being initiated, supervised machine learning methods were able, with good sensitivity and excellent specificity, to discriminate patients with PanNET from controls based on the presence of specific proteins. We were also able to differentiate patients with PanNET from patients with SI-NET. Our data supports data from a previously study we recently published, using the same methodology, where the top 12 of the 92 proteins, also used in this study, differentiated SI-NET from controls.19 This indicates that findings from this multi biomarker strategy could be applied also on patients with neuroendocrine tumours from other primaries.”

Table 1 in the “Results” section, was incorrect.

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Table 2 in the “Results” section, was incorrect.

This should have read:

Table 3 of the “Results” section was incorrect.

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