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

The use of perfusion CT for the evaluation of therapy combining AZD2171 with gefitinib in cancer patients

The purpose of this study was to determine the feasibility of dynamic contrast-enhanced perfusion CT (CTP) in evaluating the hemodynamic response of tumors in the chest and abdomen treated with a combination of AZD2171 and gefitinib. Thirteen patients were examined just before and every 4-6 weeks after starting therapy. Following intravenous injection of a contrast agent, dynamic image acquisition was obtained at the level of a selected tumor location. To calculate perfusion, the maximum-slope method was used. Pre-treatment average perfusion for extra-hepatic masses was 84 ml/min/100 g, for liver masses arterial perfusion was 25 ml/min/100 g, and a portal perfusion of 30 ml/min/100 g was found. After the administration of AZD2171 and gefitinib, in extra-hepatic masses an initial decrease in perfusion of 18% was followed by a plateau and in liver masses an initial decrease of 39% within the lesions and of 36% within a rim region surrounding the lesions was followed by a tendency to recovery of hepatic artery flow. In conclusion, CTP is feasible in showing changes of perfusion induced by anti-angiogenic therapy

Development of hypointense lesions on T1-weighted spin-echo magnetic resonance images in multiple sclerosis: Relation to inflammatory activity

Objective: To evaluate whether degree of inflammatory activity in multiple sclerosis, expressed by frequency of gadolinium enhancement, has prognostic value for development of hypointense lesions on T1-weighted spin- echo magnetic resonance images, a putative marker of tissue destruction. Design: Cohort design with long-term follow-up. Thirty-eight patients with multiple sclerosis who in the past had been monitored with monthly gadolinium-enhanced magnetic resonance imaging for a median period of 10 months (range, 6-12 months) were reexamined after a median period of 40.5 months (range, 33-80 months). Setting: Magnetic Resonance Center for Multiple Sclerosis Research, Amsterdam, the Netherlands, referral center. Main Outcome Measures: The new enhancing lesion rate (median number of gadolinium- enhancing lesions per monthly scan) during initial monthly follow-up; hypointense T1 and hyperintense T2 lesion load at first and last visit. Results: The number of enhancing lesions on entry scan correlated with the new enhancing lesions rate (r = 0.64; P<.001, Spearman rank correlation coefficient). The new enhancing lesion rate correlated with yearly increase in T1 (r = 0.42; P<.01, Spearman rank correlation coefficient) and T2 (r= 0.47; P<.01, Spearman rank correlation coefficient) lesion load. Initial T1 lesion load correlated more strongly with yearly increase in T1 lesion load (r = 0.68; P<.01, Spearman rank correlation coefficient). Conclusions: Degree of inflammatory activity only partially predicted increase in T1 (and T2) lesion load at long-term follow-up. Initial T1 lesion load strongly contributed to subsequent increase in hypointense T1 lesion load, suggesting that there is a subpopulation of patients with multiple sclerosis who are prone to develop destructive lesions

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