Potential for cure and predictors of long-term survival after radiofrequency ablation for colorectal liver metastases: A 20-years single-center experience

BACKGROUND

Additional radiofrequency ablation (RFA) of liver-limited colorectal liver metastases (CRLM) improves overall (OS) and recurrence-free survival (RFS) over systemic therapy alone. We aimed to assess the potential and predictive factors of long-term survival and cure to optimize patient selection for RFA application.

METHODS

Retrospective review of a prospectively maintained single-center database of consecutive patients undergoing RFA for liver-limited CRLM after systemic therapy between 2002 and 2020. Clinicopathologic characteristics and KRAS/BRAF-genotype data (tested routinely since 2010) were correlated to RFS and OS. Cure was defined as ≥10-years RFS (long-term survival as ≥5-years OS) following RFA.

RESULTS

For the entire cohort of 158 patients (median follow-up 13.6 years), co-occurrence of three factors, RECIST-defined response, number of ≤3 CRLM, and ≤3 cm maximum size determined a survival plateau that distinguished cured from non-cured patients (10-years RFS: 15.5% vs 0%, p < 0.0001). Among 59 patients (37.3%) being tested, 4(6.8%) were BRAF-mt, 15(25.4%) KRAS-mt, and 40(67.8%) KRAS/BRAF-wt. OS (median follow-up 8.3 years) was estimated to be higher with KRAS/BRAF-wt compared to a mutant KRAS or BRAF status (5-years OS: 22.8% vs 3.4%, p = 0.0018).

CONCLUSION

This study indicates about 15% chance of cure following RFA of low-volume liver-limited CRLM after downsizing by systemic therapy and a negative effect of KRAS or BRAF mutation on long-term survival after CRLM ablation. These findings may improve clinical decision-making in patients potentially candidate to RFA of CRLM and encourage further investigations on molecular factors determining an oligometastatic state of CRLM curable with focal ablative therapy.

P13.05.A Image annotation guideline for invivo confocal laser endomicroscopy, interrater reliability and how to learn from medical consensus for machine learning algorithms

Background

Intraoperative confocal laser endomicroscopy (CLE) is an in vivo imaging technique increasingly studied in neurosurgery and neuropathology. It can be affected by artifacts introduced by the CLE device or related to the intraoperative setting. We developed and evaluated an image annotation guideline (AGL) to detect and eliminate images bearing no valuable information as a result of such artifacts. Images ware classified into good and bad quality, based on defined technical criteria, which are also considered relevant by clinical experts.

Material and Methods

Datasets were created from intraoperative CLE in vivo specimens of patients resected for brain tumors. The process from data collection to development of the ML algorithm followed 7 steps: data quality specification, image and metadata collection, AGL development, annotation, data allocation for clinical validation, clinical validation, and, optionally, algorithm development. Final diagnoses were obtained by pathological analysis. Artifacts were grouped into three categories: diminished signal-to-noise-ratio (dSNR), optical distortions (movement/perturbations), and contrast/brightness artifacts. Images were annotated by 4 medical data annotators (T4). For clinical validation, 500 images were excluded from the training data and additionally annotated by 3 board certified neuropathologists (NPs 1-3) with experience in CLE imaging, to determine the medical consensus on good and bad images. All raters (NPs) were compared against each other and against T4; T4 was also compared against the medical consensus. Cohen’s Kappa and overall percentage agreement (OPA) were used to evaluate inter-rater reliability. Positive percent agreement (PPA) and negative percentage agreement (NPA) were also used to evaluate agreement between medical consensus and T4.

Results

21,616 CLE images and corresponding clinical metadata were collected from 94 patients and annotated. For each case between 27 and 815 CLE images were acquired over the course of the surgery (mean=175 images per case, SD=170.6). 11% and 13% of images were labeled as dSNR and distortion, respectively, and 34% as class contrast. 42% of the images represented the good quality images. Interrater agreement between the 3 NPs ranged between 0.30 and 0.59. Agreement between T4 and the medical consensus was substantial (Cohen’s Kappa &gt;=0.61). OPA between T4 and the medical consensus was 80.60%, PPA 72.34% and NPA 87.92%.

Conclusion

Annotations according to a well-structured and expertly curated AGL show higher values for Cohen’s Kappa and Overall Percent Agreement (OPA) with the medical consensus, than that of individual experts among one another. Such an AGL can be considered appropriate and produces on par results with annotations by a group of experts in the field and can be further employed for training machine learning (ML) algorithms.

Effect of sparteine sulfate on uterine prostaglandin F in the rat

Indomethacin, an inhibitor of prostaglandin synthetase, added to an in vitro bath in a concentration of 1, 5, and 10 X 10(-6) g/ml reduced sparteine-induced contractions of isolated uterine segments from pregnant rats. Contractions induced by prostaglandin F2alpha and acetylcholine were not reduced. Sparteine increased the prostaglandin F content of the blood and uterine tissue in the pregnant but not in the nonpregnant rat. This increase was significantly reduced by the administration of indomethacin (10 mg/kg). The present study suggests that the mechanism of sparteine action is mediated through a prostaglandin F system.