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

Oral biofilm models for mechanical plaque removal

In vitro plaque removal studies require biofilm models that resemble in vivo dental plaque. Here, we compare contact and non-contact removal of single and dual-species biofilms as well as of biofilms grown from human whole saliva in vitro using different biofilm models. Bacteria were adhered to a salivary pellicle for 2 h or grown after adhesion for 16 h, after which, their removal was evaluated. In a contact mode, no differences were observed between the manual, rotating, or sonic brushing; and removal was on average 39%, 84%, and 95% for Streptococcus mutans, Streptococcus oralis, and Actinomyces naeslundii, respectively, and 90% and 54% for the dual- and multi-species biofilms, respectively. However, in a non-contact mode, rotating and sonic brushes still removed considerable numbers of bacteria (24–40%), while the manual brush as a control (5–11%) did not. Single A. naeslundii and dual-species (A. naeslundii and S. oralis) biofilms were more difficult to remove after 16 h growth than after 2 h adhesion (on average, 62% and 93% for 16- and 2-h-old biofilms, respectively), while in contrast, biofilms grown from whole saliva were easier to remove (97% after 16 h and 54% after 2 h of growth). Considering the strong adhesion of dual-species biofilms and their easier more reproducible growth compared with biofilms grown from whole saliva, dual-species biofilms of A. naeslundii and S. oralis are suggested to be preferred for use in mechanical plaque removal studies in vitro

The Disease-Free Interval Between Resection of Primary Colorectal Malignancy and the Detection of Hepatic Metastases Predicts Disease Recurrence But Not Overall Survival

Introduction: The disease-free interval (DFI) between resection of primary colorectal cancer (CRC) and diagnosis of liver metastases is considered an important prognostic indicator; however, recent analyses in metastatic CRC found limited evidence to support this notion. Objective: The current study aims to determine the prognostic value of the DFI in patients with resectable colorectal liver metastases (CRLM). Methods: Patients undergoing first surgical treatment of CRLM at three academic centers in The Netherlands were eligible for inclusion. The DFI was defined as the time between resection of CRC and detection of CRLM. Baseline characteristics and Kaplan–Meier survival estimates were stratified by DFI. Cox regression analyses were performed for overall (OS) and disease-free survival (DFS), with the DFI entered as a continuous measure using a restricted cubic spline function with three knots. Results: In total, 1374 patients were included. Patients with a shorter DFI more often had lymph node involvement of the primary, more frequently received neoadjuvant chemotherapy for CRLM, and had higher number of CRLM at diagnosis. The DFI significantly contributed to DFS prediction (p =0.002), but not for predicting OS (p =0.169). Point estimates of the hazard ratio (95% confidence interval) for a DFI of 0 versus 12 months and 0 versus 24 months were 1.284 (1.114–1.480) and 1.444 (1.180–1.766), respectively, for DFS, and 1.111 (0.928–1.330) and 1.202 (0.933–1.550), respectively, for OS. Conclusion: The DFI is of prognostic value for predicting disease recurrence following surgical treatment of CRLM, but not for predicting OS outcomes

Caseload as a factor for outcome in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis

Contains fulltext : 127545.pdf (publisher's version ) (Open Access)Object Increasing evidence exists that treatment of complex medical conditions in high-volume centers is found to improve outcome. Patients with subarachnoid hemorrhage (SAH), a complex disease, probably also benefit from treatment at a high-volume center. The authors aimed to determine, based on published literature, whether a higher hospital caseload is associated with improved outcomes of patients undergoing treatment after aneurysmal subarachnoid hemorrhage. Methods The authors identified studies from MEDLINE, Embase, and the Cochrane Library up to September 28, 2012, that evaluated outcome in high-volume versus low-volume centers in patients with SAH who were treated by either clipping or endovascular coiling. No language restrictions were set. The compared outcome measure was in-hospital mortality. Mortality in studies was pooled in a random effects meta-analysis. Study quality was reported according to the GRADE (Grading of Recommendations Assessment, Development and Evaluation) criteria. Results Four articles were included in this analysis, representing 36,600 patients. The quality of studies was graded low in 3 and very low in 1. Meta-analysis using a random effects model showed a decrease in hospital mortality (OR 0.77 [95% CI 0.60-0.97]; p = 0.00; I(2) = 91%) in high-volume hospitals treating SAH patients. Sensitivity analysis revealed the relative weight of the 1 low-quality study. Removal of the study with very low quality increased the effect size of the meta-analysis to an OR of 0.68 (95% CI 0.56-0.84; p = 0.00; I(2) = 86%). The definition of hospital volume differed among studies. Cutoffs and dichotomizations were used as well as division in quartiles. In 1 study, low volume was defined as 9 or fewer patients yearly, whereas in another it was defined as fewer than 30 patients yearly. Similarly, 1 study defined high volume as more than 20 patients annually, and another defined it as more than 50 patients a year. For comparability between studies, recalculation was done with dichotomized data if available. Cross et al., 2003 (low volume /= 19) and Johnston, 2000 (low volume /= 32) provided core data for recalculation. The overall results of this analysis revealed an OR of 0.85 (95% CI 0.72-0.99; p = 0.00; I(2) = 87%). Conclusions Despite the shortcomings of this study, the mortality rate was lower in hospitals with a larger caseload. Limitations of the meta-analysis are the not uniform cutoff values and uncertainty about case mix

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.</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.</p

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

Abstract: 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 center dot Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. center dot Automatic models can accurately segment tumors in patients with colorectal liver metastases. center dot Total tumor volume can be accurately calculated based on automatic segmentations

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

Abstract 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 Abstrac