Development and validation of a prediction model of pneumothorax after CT-guided coaxial core needle lung biopsy

Development of a machine learning model for predicting pneumothorax risk in coaxial core needle biopsy (≤3 cm)

PURPOSE

The aim is to devise a machine learning algorithm exploiting preoperative clinical data to forecast the hazard of pneumothorax post-coaxial needle lung biopsy (CCNB), thereby informing clinical decision-making and enhancing perioperative care.

METHOD

This retrospective analysis aggregated clinical and imaging data from patients with lung nodules (≤3 cm) biopsies. Variable selection was done using univariate analysis and LASSO regression, with the dataset subsequently divided into training (80 %) and validation (20 %) subsets. Various machine learning (ML) classifiers were employed in a consolidated approach to ascertain the paramount model, which was followed by individualized risk profiling showcased through Shapley Additive eXplanations (SHAP).

RESULTS

Out of the 325 patients included in the study, 19.6% (64/325) experienced postoperative pneumothorax. High-risk factors determined were Cancer, Lesion_type, GOLD, Size, and Depth. The Gaussian Naive Bayes (GNB) classifier demonstrated superior prediction with an Area Under the Curve (AUC) of 0.82 (95% CI 0.71-0.94), complemented by an accuracy rate of 0.8, sensitivity of 0.71, specificity of 0.84, and an F1 score of 0.61 in the test cohort.

CONCLUSION

The formulated prognostic algorithm exhibited commendable efficacy in preoperatively prognosticating CCNB-induced pneumothorax, harboring the potential to refine personalized risk appraisals, steer clinical judgment, and ameliorate perioperative patient stewardship.

Presurgical computed tomography-guided localization of lung ground glass nodules: comparing hook-wire and indocyanine green

BACKGROUND

Presurgical computed tomography (CT)-guided localization is frequently employed to reduce the thoracotomy conversion rate, while increasing the rate of successful sublobar resection of ground glass nodules (GGNs) via video-assisted thoracoscopic surgery (VATS). In this study, we compared the clinical efficacies of presurgical CT-guided hook-wire and indocyanine green (IG)-based localization of GGNs.

METHODS

Between January 2018 and December 2021, we recruited 86 patients who underwent CT-guided hook-wire or IG-based GGN localization before VATS resection in our hospital, and compared the clinical efficiency and safety of both techniques.

RESULTS

A total of 38 patients with 39 GGNs were included in the hook-wire group, whereas 48 patients with 50 GGNs were included in the IG group. There were no significant disparities in the baseline data between the two groups of patients. According to our investigation, the technical success rates of CT-based hook-wire- and IG-based localization procedures were 97.4% and 100%, respectively (P = 1.000). Moreover, the significantly longer localization duration (15.3 ± 6.3 min vs. 11.2 ± 5.3 min, P = 0.002) and higher visual analog scale (4.5 ± 0.6 vs. 3.0 ± 0.5, P = 0.001) were observed in the hook-wire patients, than in the IG patients. Occurrence of pneumothorax was significantly higher in hook-wire patients (27.3% vs. 6.3%, P = 0.048). Lung hemorrhage seemed higher in hook-wire patients (28.9% vs. 12.5%, P = 0.057) but did not reach statistical significance. Lastly, the technical success rates of VATS sublobar resection were 97.4% and 100% in hook-wire and IG patients, respectively (P = 1.000).

CONCLUSIONS

Both hook-wire- and IG-based localization methods can effectively identified GGNs before VATS resection. Furthermore, IG-based localization resulted in fewer complications, lower pain scores, and a shorter duration of localization.

Score to Predict the Occurrence of Pneumothorax After Computed Tomography-guided Percutaneous Transthoracic Lung Biopsy

PURPOSE

The main objective of this study was to identify risk factors for post-percutaneous transthoracic lung biopsy (PTLB) pneumothorax and to establish and validate a predictive score for pneumothorax occurrence to identify patients eligible for outpatient care.

MATERIAL AND METHODS

Patients who underwent PTLB between November 1, 2012 and March 1, 2017 were retrospectively evaluated for clinical and radiologic factors potentially related to pneumothorax occurrence. Multivariate logistic regression was used to identify risk factors, and the model coefficient for each factor was used to compute a score. Then, a validation cohort was prospectively evaluated from March 2018 to October 2019.

RESULTS

Among the 498 eligible patients in the study cohort, pneumothorax occurred in 124 patients (24.9%) and required drainage in 34 patients (6.8%). Pneumothorax risk factors were chronic obstructive pulmonary disease (OR 95% CI 2.28[1.18-4.43]), several passages through the pleura (OR 95% CI 7.71[1.95-30.48]), an anterior biopsy approach (OR 95% CI 6.36 3.82-10.58]), skin-to-pleura distance ≤30 mm (OR 95% CI 2.25[1.09-6.65]), and aerial effusion >10 mm (OR 95% CI 9.27 [5.16-16.65]). Among the 236 patients in the prospective validation cohort, pneumothorax occurred in 18% and 8% were drained. A negative score (<73 points) predicted a probability of pneumothorax occurrence of 7.4% and late evacuation of 2.5% (OR 95% CI respectively 0.18[0.08-0.39] and 0.15[0.04-0.55]) and suggested a reduced length of hospital stay (P=0.009).

CONCLUSION

This predictive score for pneumothorax secondary to PTLB has high prognostic performance and accuracy to direct patients toward outpatient management.

CLINICAL TRIALS

NCT03488043.