A novel risk classification model integrating CEA, ctDNA, and pTN stage for stage 3 colon cancer: a post hoc analysis of the IDEA-France trial.

BACKGROUND: We assessed the added value of incorporating carcinoembryonic antigen (CEA) to circulating tumor DNA (ctDNA) and pathological TN (pTN) stage for risk classification in stage 3 colon cancer (CC). PATIENTS AND METHODS: We retrospectively analyzed postoperative CEA values in patients with CC from the IDEA-France phase 3 trial. The relation between disease-free survival (DFS) and CEA was modeled through restricted cubic splines. Prognostic value of CEA, ctDNA, and pTN was assessed with the Kaplan-Meier method. Multivariate analysis was used to identify prognostic and predictive factors for DFS. RESULTS: Among 696 patients (35%), CEA values were retrievable, and for 405 (20%) both CEA and ctDNA were available. An optimized CEA threshold of 2 ng/mL was identified, the 3-year DFS was 66.4% for patients above the threshold and 80.9% for those below (HR, 1.74; 95% CI, 1.33-2.28, P < .001). In multivariate analysis, CEA ≥ 2 ng/mL contributed significantly to model variability, becoming an independent prognostic factor for DFS (HR, 1.82; 95% CI,1.27-2.59), alongside ctDNA (HR, 1.88; 95% CI, 1.16-3.03) and pTN (HR, 1.78; 95% CI, 1.24-2.54). A novel integrated risk classification combining CEA, ctDNA, and pTN stage reclassified 19.8% of pT4/N2 patients as low risk and 2.5% of pT3/N1 patients as high risk. This new classification demonstrated the 3-year DFS of 80.8% for low-risk patients and 55.4% for high-risk patients (HR, 2.66, 95% CI, 1.84-3.86, P < .001). CONCLUSIONS: Postoperative CEA value is a prognostic factor for DFS in stage 3 CC, independently of ctDNA and pTN. It advocates for systematic reporting in future adjuvant trials. Integrating both biomarkers with pTN could refine risk classification in stage 3 CC.

Survival Outcomes in Patients with Monobloc-Resected Stage IIC (pT4bN0) Colon Cancer: A Retrospective Observational Cohort Study.

BACKGROUND: Stage II colon cancer (CC) exhibits considerable prognostic heterogeneous. Our objective was to assess survival but also the prognosis impact of microsatellite instability (MSI) in patients with stage IIC (T4bN0M0) CC. PATIENTS AND METHODS: We conducted a retrospective observational study including all patients who had primary stage IIC CC resection between 2010 and 2020 in 2 expert centers. The primary endpoint was overall survival (OS) and disease-free survival (DFS) and time-to-relapse (TTR) were secondary endpoints. RESULTS: Sixty-six patients, median age of 74 years [30-95], were included, with 37.9% presenting MSI (n = 25). Organ invasion involved the last ileal loop (n = 17), another colonic segment (n = 15), omentum (n = 13), visceral peritoneum (n = 13), and the bladder (n = 4). Surgical quality criteria showed complete monobloc resection in all patients and 93.9% R0 resection. After a median follow-up of 5 years [3.5-6.6], the entire population showed a 5-year OS of 65.2% [53.0-80.3] and 5-year DFS of 53.5% [41.1-69.6], with 18.9% [6.8-29.4] experiencing relapses at 5 years. The MSI phenotype correlated with improved 5-year OS (75.5% [56.5-100] vs. 59.5% [44.9-79.0], HR 0.41 [0.17-0.99]; P = .04), but DFS and TTR did not differ. Adjuvant chemotherapy was administered to 34.9% of patients. Univariate analysis identified age > 65 years, MSI status, and the number of nodes as factors associated with OS. CONCLUSION: These data underline, in relation to a low rate of relapse, the lack of consensus regarding the appropriate indication for adjuvant chemotherapy in this high-risk stage II population.

2D harmonic filtering of MR phase images in multicenter clinical setting: toward a magnetic signature of cerebral microbleeds.

Cerebral microbleeds (CMBs) have emerged as a new imaging marker of small vessel disease. Composed of hemosiderin, CMBs are paramagnetic and can be detected with MRI sequences sensitive to magnetic susceptibility (typically, gradient recalled echo T2* weighted images). Nevertheless, their identification remains challenging on T2* magnitude images because of confounding structures and lesions. In this context, T2* phase image may play a key role in better characterizing CMBs because of its direct relationship with local magnetic field variations due to magnetic susceptibility difference. To address this issue, susceptibility-based imaging techniques were proposed, such as Susceptibility Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM). But these techniques have not yet been validated for 2D clinical data in multicenter settings. Here, we introduce 2DHF, a fast 2D phase processing technique embedding both unwrapping and harmonic filtering designed for data acquired in 2D, even with slice-to-slice inconsistencies. This method results in internal field maps which reveal local field details due to magnetic inhomogeneity within the region of interest only. This technique is based on the physical properties of the induced magnetic field and should yield consistent results. A synthetic phantom was created for numerical simulations. It simulates paramagnetic and diamagnetic lesions within a 'brain-like' tissue, within a background. The method was evaluated on both this synthetic phantom and multicenter 2D datasets acquired in standardized clinical setting, and compared with two state-of-the-art methods. It proved to yield consistent results on synthetic images and to be applicable and robust on patient data. As a proof-of-concept, we finally illustrate that it is possible to find a magnetic signature of CMBs and CMCs on internal field maps generated with 2DHF on 2D clinical datasets that give consistent results with CT-scans in a subsample of 10 subjects acquired with both modalities.

Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

White matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM), and did not need any training set.