Anesthetic management of precise radiotherapy under apnea-like condition

High-Frequency Jet Ventilation Versus Spontaneous Respiration for Percutaneous Cryoablation of Lung Tumors: Comparison of Adverse Events and Procedural Efficiency

BACKGROUND. High-frequency jet ventilation (HFJV) facilitates accurate probe placement in percutaneous ablation of lung tumors but may increase risk for adverse events, including systemic air embolism. OBJECTIVE. The purpose of this study was to compare major adverse events and procedural efficiency of percutaneous lung ablation with HFJV under general anesthesia to spontaneous respiration (SR) under moderate sedation. METHODS. This retrospective study included consecutive adults who underwent CT-guided percutaneous cryoablation of one or more lung tumors with HFJV or SR between January 1, 2017, and May 31, 2023. We compared major adverse events (Common Terminology Criteria for Adverse Events grade ≥ 3) within 30 days postprocedure and hospital length of stay (HLOS) of 2 days or more using logistic regression analysis. We compared procedure time, room time, CT guidance acquisition time, CT guidance radiation dose, total radiation dose, and pneumothorax using generalized estimating equations. RESULTS. Overall, 139 patients (85 women, 54 men; median age, 68 years) with 310 lung tumors (82% metastases) underwent 208 cryoablations (HFJV, n = 129; SR, n = 79). HFJV showed greater rates than SR for the treatment of multiple tumors per session (43% vs 19%, respectively; p = .02) and tumors in a nonperipheral location (48% vs 24%, p < .001). Major adverse event rate was 8% for HFJV and 5% for SR (p = .46). No systemic air embolism occurred. HLOS was 2 days or more in 17% of sessions and did not differ significantly between HFJV and SR (p = .64), including after adjusting for probe number per session, chronic obstructive pulmonary disease, and operator experience (p = .53). Ventilation modalities showed no significant difference in procedure time, CT guidance acquisition time, CT guidance radiation dose, or total radiation dose (all p > .05). Room time was longer for HFJV than SR (median, 154 vs 127 minutes, p < .001). For HFJV, the median anesthesia time was 136 minutes. Ventilation modalities did not differ in the frequencies of pneumothorax or pneumothorax requiring chest tube placement (both p > .05). CONCLUSION. HFJV appears to be as safe as SR but had longer room times. HFJV can be used in complex cases without significantly impacting HLOS of 2 days or more, procedure time, or radiation exposure. CLINICAL IMPACT. Selection of the ventilation modality during percutaneous lung ablation should be based on patient characteristics and anticipated procedural requirements as well as operator preference.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma

OBJECTIVE

To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model.

METHODS

This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model.

RESULTS

The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001).

CONCLUSION

The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.