Short-Term Outcomes of Secondary Liver Surgery for Initially Unresectable Colorectal Liver Metastases following Modern Induction Systemic Therapy in the Dutch CAIRO5 Trial

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

Prognostic value of total tumor volume in patients with colorectal liver metastases:A secondary analysis of the randomized CAIRO5 trial with external cohort validation

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 &lt; 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.</p

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

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. Graphical Abstract: [Figure not available: see fulltext.]

Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation

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

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review

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

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review

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

Short-Term Outcomes of Secondary Liver Surgery for Initially Unresectable Colorectal Liver Metastases following Modern Induction Systemic Therapy in the Dutch CAIRO5 Trial

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