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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.

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: 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.)

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.

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.)

Background: The coronavirus disease 2019 (COVID-19) pandemic is a challenge for intensive care units (ICU) in part due to the failure to identify risks for patients early and the inability to render an accurate prognosis. Previous reports suggest a strong association between hypercoagulability and poor outcome. Factors related to hemostasis may, therefore, serve as tools to improve the management of COVID-19 patients., Aim: The purpose of this report is to develop a model to determine whether it is possible to early identify COVID-19 patients at risk for thromboembolic complications (TCs)., Methods: We analyzed electronic health record data of 108 consecutive COVID-19 patients admitted to the adult ICU of the Erasmus University Medical Center between February 27 and May 20, 2020. By training a decision tree classifier on 66% of the available data, a model for the prediction of TCs was developed., Results: The median (interquartile range) age was 62 (53-70) years and 73% were male. Forty-three patients (40%) developed a TC during their ICU stay. Mortality was higher for patients in the TCs group compared to the control group (26% vs. 8%, P =0.03). Lactate dehydrogenase, standardized bicarbonate, albumin, and leukocytes were identified by the Decision Tree classifier as the most powerful predictors for TCs 2 days before the onset of the TC, with a sensitivity of 73% and a positive likelihood ratio of 2.7 on the test dataset., Conclusions: Clinically relevant TCs frequently occur in critically ill COVID-19 patients. These can successfully be predicted using a decision tree model. Although this model could be of special importance to aid clinical decision making, its generalizability and clinical impact should be determined in a larger population., Relevance for Patients: Recently, severe TCs were observed in COVID-19 patients with progressive respiratory failure warranting ICU treatment. Timely identification of patients at risk of developing TCs is critical inasmuch as it would enable clinicians to initiate potentially salvaging therapeutic anticoagulation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright: © Whioce Publishing Pte. Ltd.)

Background: ALPPS is a two-stage hepatectomy that induces more rapid liver growth compared to conventional strategies. This report aims to establish a risk-score to avoid adverse outcomes of ALPPS only for patients with colorectal liver metastases (CRLM) as primary indication for ALPPS., Methods: All patients with CRLM included in the ALPPS registry were included. Risk score analysis was performed for 90-day mortality after ALPPS, defined as death within 90 days after either stage. Two risk scores were generated i.e. one for application before stage-1, and one for application before stage-2. Logistic regression analysis was performed to establish the risk-score., Results: In total, 486 patients were included, of which 35 (7%) died 90 days after stage-1 or 2. In the stage-1 risk score, age ≥67 years (OR 3.7), FLR/BW ratio <0.40 (OR 2.9) and total center-volume (OR 2.4) were included. For the stage-2 score age ≥67 years (OR 3.7), FLR/BW ratio <0.40 (OR 2.8), bilirubin 5 days after stage-1 >50 μmol/L (OR 2.4), and stage-1 morbidity grade IIIA or higher (OR 6.3) were included., Conclusions: The CRLM risk-score to predict mortality after ALPPS demonstrates that older patients with small remnant livers in inexperienced centers, especially after experiencing morbidity after stage-1 have adverse outcomes. The risk score may be used to restrict ALPPS to low-risk patient populations., (Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Background and Aim: Both the number and complexity of medical trials are increasing vastly. To facilitate easy access to concise trial information, a freely available mobile application including all ongoing clinical trials of the Dutch Colorectal Cancer Group (DCCG) was developed. The aim of this study was to investigate the use and user satisfaction over the first 2 years., Methods: The application was launched in January 2015 on iOS and Android platforms. Google Analytics was used to monitor anonymous user data up to February 2017. In addition, an online survey regarding the use and satisfaction among health-care professionals and research affiliates active in the field of colorectal cancer in the Netherlands was conducted., Results: A total of 6173 unique users were identified, of which 1822 (30%) were from the Netherlands, representing a total of 16,065 and 10,987 (68%) sessions, respectively. The median session duration per day was 01:47 min (IQR 0:51-03:03). The mobile application was mostly used on Monday, Tuesday, and Thursday, and the number of sessions was highest during the following time frames: 12-13 pm (9%), 17-18 pm (9%), and 13-14 pm (8%). Of 121 survey responses, most were medical doctors (47%), nurses (25%), or researchers (9%), working either in a teaching (40%), academic hospital (32%), or general hospital (19%). 83% of all respondents rated the application 4 or higher for satisfaction on a 5-point scale. Highest reported reasons of the use were urgent trial inquiry (57%) and usage during multi-disciplinary meetings (49%)., Conclusion: The DCCG Trials application is frequently used, and the majority of users is highly satisfied., Relevance for Patients: Clustering trial information into one platform, such as DCCG trials app, has shown to be useful for medical professionals treating patients with colorectal carcinoma in the Netherlands.

Background: Discrepancies are often noted between management of perihilar cholangiocarcinoma (PHC) in regional hospitals and the eventual treatment plan in specialized centers., Objective: The objective of this article is to evaluate whether regional centers adhere to guideline recommendations following implementation in 2013., Methods: Data were analyzed from all consecutive patients with suspected PHC referred to our academic center between June 2013 and December 2015. Frequency and quality of biliary drainage and imaging at referring centers were assessed as well as the impact of inadequate initial drainage., Results: Biliary drainage was attempted at regional centers in 83 of 158 patients (52.5%), with a technical and therapeutic success rate of 79.5% and 50%, respectively, and a complication rate of 45.8%. The computed tomography protocol was not in accordance with guidelines in 52.8% of referrals. In 45 patients (54.2%) who underwent drainage in regional centers, additional drainage procedures were required after referral. Initial inadequate biliary drainage at a regional center was significantly associated with more procedures and a prolonged waiting time until surgery. A trend toward more drainage-related complications was observed among patients with inadequate initial drainage (54.7% vs. 39.0%, p  = 0.061)., Conclusion: Despite available guidelines, suboptimal management of PHC persists in many regional centers and affects eventual treatment strategies.

Background: Transaminase levels are usually measured as markers of hepatocellular injury following liver resection, but recent evidence was unclear on their clinical value. This study aimed to identify factors that determine peak postoperative transaminase levels and correlated transaminase levels to postoperative complications., Study Design: All liver resections performed at a single center between 2006 and 2015 were included in the analysis. Multivariate analysis was used to identify factors that determine peak ALT and AST levels and postoperative morbidity and mortality. An ALT and AST cutoff for the prediction of mortality was determined using receiver operating characteristic curves analysis., Results: A total of 539 resections were included. Clavien-Dindo grade III or higher complications, intraoperative transfusion, and operative duration were identified as determinants of peak transaminases. A peak AST cut-off value for predicting mortality was defined at 828 U/L, with an area under the curve of 0.81 (0.73-0.89). The cut-off was an independent predictor of mortality (P < 0.01) along with (intraoperative) transfusion (P < 0.01), fifty-fifty criteria (P < 0.01), and age (P < 0.01)., Conclusion: Postoperative transaminase levels are independent predictors of postoperative morbidity and mortality and therefore clinically relevant. Transaminase levels usually peak during the first 24 h after surgery and thus possess early prognostic power in terms of postoperative mortality., (Copyright © 2016 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Background: Colorectal cancer patients with unresectable liver-only metastases may be cured after downsizing of metastases by neoadjuvant systemic therapy. However, the optimal neoadjuvant induction regimen has not been defined, and the lack of consensus on criteria for (un)resectability complicates the interpretation of published results., Methods/design: CAIRO5 is a multicentre, randomised, phase 3 clinical study. Colorectal cancer patients with initially unresectable liver-only metastases are eligible, and will not be selected for potential resectability. The (un)resectability status is prospectively assessed by a central panel consisting of at least one radiologist and three liver surgeons, according to predefined criteria. Tumours of included patients will be tested for RAS mutation status. Patients with RAS wild type tumours will be treated with doublet chemotherapy (FOLFOX or FOLFIRI) and randomised between the addition of either bevacizumab or panitumumab, and patients with RAS mutant tumours will be randomised between doublet chemotherapy (FOLFOX or FOLFIRI) plus bevacizumab or triple chemotherapy (FOLFOXIRI) plus bevacizumab. Radiological evaluation to assess conversion to resectability will be performed by the central panel, at an interval of two months. The primary study endpoint is median progression-free survival. Secondary endpoints are the R0/1 resection rate, median overall survival, response rate, toxicity, pathological response of resected lesions, postoperative morbidity, and correlation of baseline and follow-up evaluation with respect to outcomes by the central panel., Discussion: CAIRO5 is a prospective multicentre trial that investigates the optimal systemic induction therapy for patients with initially unresectable, liver-only colorectal cancer metastases., Trial Registration: CAIRO 5 is registered at European Clinical Trials Database (EudraCT) (2013-005435-24). CAIRO 5 is registered at ClinicalTrials.gov: NCT02162563 , June 10, 2014.