Lymph node regression after neoadjuvant chemoradiotherapy in rectal cancer.

AIMS: Lymph node metastases (LNM) are one of the most important prognostic indicators in solid tumours and a major component of cancer staging. Neoadjuvant therapy might influence nodal status by induction of regression. Our aim is to determine the prevalence and role of regression of LNM on outcomes in patients with rectal cancer. METHODS AND RESULTS: Four independent study populations of rectal cancer patients treated with similar regimens of chemoradiotherapy were pooled together to obtain a total cohort of 469 patients. Post-treatment nodal status (ypN) and signs of tumour regression (Reg) were incorporated to form three-tiered (ypN- Reg+, ypN- Reg- and ypN+) and four-tiered (ypN- Reg+, ypN- Reg-, ypN+ Reg+ and ypN+ Reg-) classifications. In our cohort, 31% of patients presented with ypN+ rectal cancer. As expected, we found significantly worse overall survival (OS) in ypN+ patients compared to ypN- patients (P = 0.002). The percentage of ypN- patients with lymph nodes with complete regression was 20% in our cohort. While node-negative patients with and without regression had similar OS (P = 0.09), disease-free survival (DFS) was significantly better in node-negative patients with regression (P = 0.009). CONCLUSIONS: Regression in lymph nodes is frequent, and node-negative patients with evidence of lymph node regression have better DFS compared to node-negative patients without such evidence.

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients.

The application of non-linear signal analysis techniques to biomedical data is key to improve our knowledge about complex physiological and pathological processes. In particular, the use of non-linear techniques to study electroencephalographic (EEG) recordings can provide an advanced characterization of brain dynamics. In epilepsy these dynamics are altered at different spatial scales of neuronal organization. We therefore apply non-linear signal analysis to EEG recordings from epilepsy patients derived with intracranial hybrid electrodes, which are composed of classical macro contacts and micro wires. Thereby, these electrodes record EEG at two different spatial scales. Our aim is to test the degree to which the analysis of the EEG recorded at these different scales allows us to characterize the neuronal dynamics affected by epilepsy. For this purpose, we retrospectively analyzed long-term recordings performed during five nights in three patients during which no seizures took place. As a benchmark we used the accuracy with which this analysis allows determining the hemisphere that contains the seizure onset zone, which is the brain area where clinical seizures originate. We applied the surrogate-corrected non-linear predictability score (ψ), a non-linear signal analysis technique which was shown previously to be useful for the lateralization of the seizure onset zone from classical intracranial EEG macro contact recordings. Higher values of ψ were found predominantly for signals recorded from the hemisphere containing the seizure onset zone as compared to signals recorded from the opposite hemisphere. These differences were found not only for the EEG signals recorded with macro contacts, but also for those recorded with micro wires. In conclusion, the information obtained from the analysis of classical macro EEG contacts can be complemented by the one of micro wire EEG recordings. This combined approach may therefore help to further improve the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.

Inferring directed networks using a rank-based connectivity measure.

Inferring the topology of a network using the knowledge of the signals of each of the interacting units is key to understanding real-world systems. One way to address this problem is using data-driven methods like cross-correlation or mutual information. However, these measures lack the ability to distinguish the direction of coupling. Here, we use a rank-based nonlinear interdependence measure originally developed for pairs of signals. This measure not only allows one to measure the strength but also the direction of the coupling. Our results for a system of coupled Lorenz dynamics show that we are able to consistently infer the underlying network for a subrange of the coupling strength and link density. Furthermore, we report that the addition of dynamical noise can benefit the reconstruction. Finally, we show an application to multichannel electroencephalographic recordings from an epilepsy patient.