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Background: This study aimed to assess the prognostic value of total tumor volume (TTV) for early recurrence (within 6 months) and overall survival (OS) in patients with colorectal liver metastases (CRLM), treated with induction systemic therapy followed by complete local treatment., Methods: Patients with initially unresectable CRLM from the multicenter randomized phase 3 CAIRO5 trial (NCT02162563) who received induction systemic therapy followed by local treatment were included. Baseline TTV and change in TTV as response to systemic therapy were calculated using the CT scan before and the first after systemic treatment, and were assessed for their added prognostic value. The findings were validated in an external cohort of patients treated at a tertiary center., Results: In total, 215 CAIRO5 patients were included. Baseline TTV and absolute change in TTV were significantly associated with early recurrence (P = 0.005 and P = 0.040, respectively) and OS in multivariable analyses (P = 0.024 and P = 0.006, respectively), whereas RECIST1.1 was not prognostic for early recurrence (P = 0.88) and OS (P = 0.35). In the validation cohort (n = 85), baseline TTV and absolute change in TTV remained prognostic for early recurrence (P = 0.041 and P = 0.021, respectively) and OS in multivariable analyses (P < 0.0001 and P = 0.012, respectively), and showed added prognostic value over conventional clinicopathological variables (increase C-statistic, 0.06; 95 % CI, 0.02 to 0.14; P = 0.008)., Conclusion: Total tumor volume is strongly prognostic for early recurrence and OS in patients who underwent complete local treatment of initially unresectable CRLM, both in the CAIRO5 trial and the validation cohort. In contrast, RECIST1.1 did not show prognostic value for neither early recurrence nor OS., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors of this manuscript declare relationships with the following companies: C.J.A.P. has an advisory role for Nordic Pharma; SAS Analytics paid for traveling expenses G. Kazemier. This funding is not related to the current research. The remaining authors declare no potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM)., Methods: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated., Results: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation., Conclusions: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients., Relevance Statement: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency., Key Points: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations., (© 2023. The Author(s).)

For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62-0.92), 0.77 (95%CI 0.64-0.90), 0.72 (95%CI 0.57-0.87), and 0.86 (95%CI 0.76-0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47-0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.

Background: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept., Methods: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value., Results: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81-0.85]), sensitivity of 77.9% (0.67-0.87), specificity of 79.2% (0.72-0.85), positive predictive value of 61.6% (0.54-0.69), and negative predictive value of 89.3% (0.85-0.93)., Conclusion: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose To evaluate interobserver variability in the morphologic tumor response assessment of colorectal liver metastases (CRLM) managed with systemic therapy and to assess the relation of morphologic response with gene mutation status, targeted therapy, and Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 measurements. Materials and Methods Participants with initially unresectable CRLM receiving different systemic therapy regimens from the randomized, controlled CAIRO5 trial (NCT02162563) were included in this prospective imaging study. Three radiologists independently assessed morphologic tumor response on baseline and first follow-up CT scans according to previously published criteria. Two additional radiologists evaluated disagreement cases. Interobserver agreement was calculated by using Fleiss κ. On the basis of the majority of individual radiologic assessments, the final morphologic tumor response was determined. Finally, the relation of morphologic tumor response and clinical prognostic parameters was assessed. Results In total, 153 participants (median age, 63 years [IQR, 56-71]; 101 men) with 306 CT scans comprising 2192 CRLM were included. Morphologic assessment performed by the three radiologists yielded 86 (56%) agreement cases and 67 (44%) disagreement cases (including four major disagreement cases). Overall interobserver agreement between the panel radiologists on morphology groups and morphologic response categories was moderate (κ = 0.53, 95% CI: 0.48, 0.58 and κ = 0.54, 95% CI: 0.47, 0.60). Optimal morphologic response was particularly observed in patients treated with bevacizumab ( P = .001) and in patients with RAS/BRAF mutation ( P = .04). No evidence of a relationship between RECIST 1.1 and morphologic response was found ( P = .61). Conclusion Morphologic tumor response assessment following systemic therapy in participants with CRLM demonstrated considerable interobserver variability. Keywords: Tumor Response, Observer Performance, CT, Liver, Metastases, Oncology, Abdomen/Gastrointestinal Clinical trial registration no. NCT02162563 Supplemental material is available for this article. © RSNA, 2022.

Objective: To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC., Summary of Background Data: Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection., Methods: A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score., Results: After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36., Conclusions: The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research., Methods: We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine., Conclusion: This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside., Competing Interests: Competing interests: DG received speaker's fees and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–2012), Maquet, and Novalung (medical advisory board 2015–2018). JH currently works as industry expert healthcare at SAS Institute. EvU currently works as principal analytics consultant at SAS Institute. No financial relationships exist that could be construed as a potential conflict of interest. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Somatic KRAS mutations occur in approximately half of the patients with metastatic colorectal cancer (mCRC). Biologic tumor characteristics differ on the basis of the KRAS mutation variant. KRAS mutations are known to influence patient prognosis and are used as predictive biomarker for treatment decisions. This study examined clinical features of patients with mCRC with a somatic mutation in KRAS G12, G13, Q61, K117, or A146., Methods: A total of 419 patients with colorectal cancer with initially unresectable liver-limited metastases, who participated in a multicenter prospective trial, were evaluated for tumor tissue KRAS mutation status. For the subgroup of patients who carried a KRAS mutation and were treated with bevacizumab and doublet or triplet chemotherapy (N = 156), pretreatment circulating tumor DNA levels were analyzed, and total tumor volume (TTV) was quantified on the pretreatment computed tomography images., Results: Most patients carried a KRAS G12 mutation (N = 112), followed by mutations in G13 (N = 15), A146 (N = 12), Q61 (N = 9), and K117 (N = 5). High plasma circulating tumor DNA levels were observed for patients carrying a KRAS A146 mutation versus those with a KRAS G12 mutation, with median mutant allele frequencies of 48% versus 19%, respectively. Radiologic TTV revealed this difference to be associated with a higher tumor load in patients harboring a KRAS A146 mutation (median TTV 672 cm 3 [A146] v 74 cm 3 [G12], P = .036). Moreover, KRAS A146 mutation carriers showed inferior overall survival compared with patients with mutations in KRAS G12 (median 10.7 v 26.4 months; hazard ratio = 2.5; P = .003)., Conclusion: Patients with mCRC with a KRAS A146 mutation represent a distinct molecular subgroup of patients with higher tumor burden and worse clinical outcomes, who might benefit from more intensive treatments. These results highlight the importance of testing colorectal cancer for all KRAS mutations in routine clinical care., Competing Interests: Remond J. A. Fijneman Research Funding: Merck BV (Inst), Personal Genome Diagnostics (Inst), Delfi Diagnostics (Inst), Cergentis (Inst) Patents, Royalties, Other Intellectual Property: Several Patents pending (Inst) No other potential conflicts of interest were reported. Remond J. A. Fijneman Research Funding: Merck BV (Inst), Personal Genome Diagnostics (Inst), Delfi Diagnostics (Inst), Cergentis (Inst) Patents, Royalties, Other Intellectual Property: Several Patents pending (Inst) No other potential conflicts of interest were reported., (© 2021 by American Society of Clinical Oncology.)

Objectives: Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM)., Background: RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV., Methods: Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment., Results: A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease ( P = 0.015), while RECIST1.1 was not prognostic ( P = 0.821)., Conclusions: TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM., Competing Interests: C.J.A.P. has an advisory role for Nordic Pharma. This funding is not related to the current research. The remaining authors declare no potential conflicts of interest. The CAIRO5 study is supported by unrestricted scientific grants from Roche and Amgen. The funders had no role in the design, conduct, and submission of the study, or in the decision to submit the manuscript for publication., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Histologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach., Methods: From 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy., Results: The highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82., Conclusions: This study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.

Background: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day., Methods: This study included all adult patients discharged from surgical care in the surgical oncology department from June 2017 to February 2020. The primary outcome was to predict whether a patient still needs hospital-specific interventional care beyond the second postoperative day. Hospital-specific care was defined as unplanned reoperations, radiological interventions, and intravenous antibiotics administration. Different analytical methods were compared with respect to the area under the receiver-operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value., Results: Each model was trained on 1,174 episodes. In total, 847 (50.5%) patients required an intervention during postoperative admission. A random forest model performed best with an area under the receiver-operating characteristics curve of 0.88 (95% confidence interval 0.83-0.93), sensitivity of 79.1% (95% confidence interval 0.67-0.92), specificity of 80.0% (0.73-0.87), positive predictive value of 57.6% (0.45-0.70) and negative predictive value of 91.7% (0.87-0.97)., Conclusion: This proof-of-concept study found that a random forest model could successfully predict whether a patient could be safely discharged to a nursing home and does not need hospital care anymore. Such a model could aid hospitals in addressing capacity challenges and improve patient flow, allowing for timely surgical care., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future., Competing Interests: Competing interests: DG has received speakers fees and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–12), Maquet and Novalung (medical advisory board 2015–18). JH currently works as industry expert healthcare at SAS Institute. No financial relationships exists that could be construed as a potential conflict of interest. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Recurrence rates after resection of colorectal cancer liver metastases (CRLM) are high and correlate with worse survival. Postoperative circulating tumour DNA (ctDNA) is a promising prognostic biomarker. Focusing on patients with resected CRLM, this study aimed to evaluate the association between the detection of postoperative ctDNA, pathologic response and recurrence-free survival (RFS)., Methods: Twenty-three patients were selected from an ongoing phase-3 trial who underwent resection of RAS-mutant CRLM after induction systemic treatment. CtDNA analysis was performed by droplet digital PCR using blood samples collected at baseline, before and after resection. Pathologic response of CRLM was determined via the Tumour Regression Grading system., Findings: With a median follow-up of 19.6 months, the median RFS for patients with detectable (N = 6, [26%]) and undetectable (N = 17, [74%]) postoperative ctDNA was 4.8 versus 12.1 months, respectively. Among 21 patients with available tumour tissue, pathologic response in patients with detectable compared to undetectable postoperative ctDNA was found in one of six (17%) and 15 of 15 (100%) patients, respectively (p < 0.001). In univariable Cox regression analyses both postoperative detectable ctDNA (HR = 3.3, 95%CI = 1.1-9.6, p = 0.03) and pathologic non-response (HR = 4.6, 95%CI = 1.4-15, p = 0.01) were associated with poorer RFS and were strongly correlated (r = 0.88, p < 0.001). After adjusting for clinical characteristics in pairwise multivariable analyses, postoperative ctDNA status remained associated with RFS., Interpretation: The detection of postoperative ctDNA after secondary resection of CRLM is a promising prognostic factor for RFS and appeared to be highly correlated with pathologic response., Funding: None., Competing Interests: Declaration of Competing Interest C.J.A.P. has an advisory role for Nordic Pharma. This funding is not related to the current research. G.A.M. reports non-financial support from Exact Sciences, non-financial support from Sysmex, non-financial support from Sentinel CH. SpA, non-financial support from Personal Genome Diagnostics (PGDX), other from Hartwig Medical Foundation, grants from CZ (OWM Centrale Zorgverzekeraars groep Zorgverzekeraar u.a), other from Royal Philips, other from GlaxoSmithKline, other from Keosys SARL, other from Open Clinica LLC, other from Roche Diagnostics Nederland BV, other from The Hyve BV, other from Open Text, other from SURFSara BV, other from Vancis BV, other from CSC Computer Sciences BV, outside the submitted work; In addition, G.A.M. has several patents pending. R.J.A.F. reports grants and non-financial support from Personal Genome Diagnostics, grants from MERCK BV, non-financial support from Pacific Biosciences, non-financial support from Cergentis BV, outside the submitted work; In addition, R.J.A.F. has several patents pending. The remaining authors declare no potential conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To present short-term outcomes of liver surgery in patients with initially unresectable colorectal liver metastases (CRLM) downsized by chemotherapy plus targeted agents., Background: The increase of complex hepatic resections of CRLM, technical innovations pushing boundaries of respectability, and use of intensified induction systemic regimens warrant for safety data in a homogeneous multicenter prospective cohort., Methods: Patients with initially unresectable CRLM, who underwent complete resection after induction systemic regimens with doublet or triplet chemotherapy, both plus targeted therapy, were selected from the ongoing phase III CAIRO5 study (NCT02162563). Short-term outcomes and risk factors for severe postoperative morbidity (Clavien Dindo grade ≥ 3) were analyzed using logistic regression analysis., Results: A total of 173 patients underwent resection of CRLM after induction systemic therapy. The median number of metastases was 9 and 161 (93%) patients had bilobar disease. Thirty-six (20.8%) 2-stage resections and 88 (51%) major resections (>3 liver segments) were performed. Severe postoperative morbidity and 90-day mortality was 15.6% and 2.9%, respectively. After multivariable analysis, blood transfusion (odds ratio [OR] 2.9 [95% confidence interval (CI) 1.1-6.4], P = 0.03), major resection (OR 2.9 [95% CI 1.1-7.5], P = 0.03), and triplet chemotherapy (OR 2.6 [95% CI 1.1-7.5], P = 0.03) were independently correlated with severe postoperative complications. No association was found between number of cycles of systemic therapy and severe complications ( r = -0.038 , P = 0.31)., Conclusion: In patients with initially unresectable CRLM undergoing modern induction systemic therapy and extensive liver surgery, severe postoperative morbidity and 90-day mortality were 15.6% and 2.7%, respectively. Triplet chemotherapy, blood transfusion, and major resections were associated with severe postoperative morbidity., Competing Interests: Disclosure: C.J.A.P. has an advisory role for Nordic Pharma. The other authors declare that they have nothing to disclose. The CAIRO5 study (NCT02162563) was supported by unrestricted scientific grants from Roche and Amgen. The funders had no role in the design, conduct and submission of the study, nor in the decision to submit the manuscript for publication., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)