Assessment of safety margin after microwave ablation of stage I NSCLC with three-dimensional reconstruction technique using CT imaging

2024 multidisciplinary consensus on image-guided lung tumor ablation from the Taiwan Academy of Tumor Ablation

In this article, the multidisciplinary team of the Taiwan Academy of Tumor Ablation, who have expertise in treating lung cancer, present their perspectives on percutaneous image-guided thermal ablation (IGTA) of lung tumors. The modified Delphi technique was applied to reach a consensus on clinical practice guidelines concerning ablation procedures, including a comprehensive literature review, selection of panelists, creation of a rating form and survey, and arrangement of an in-person meeting where panelists agreed or disagreed on various points. The conclusion was a final rating and written summary of the agreement. The multidisciplinary expert team agreed on 10 recommendations for the use of IGTA in the lungs. These recommendations include terms and definitions, line of treatment planning, modality, facility rooms, patient anesthesia settings, indications, margin determination, post-ablation image surveillance, qualified centers, and complication ranges. In summary, IGTA is a safe and feasible approach for treating primary and metastatic lung tumors, with a relatively low complication rate. However, decisions regarding the ablation technique should consider each patient's specific tumor characteristics.

Efficacy and Safety of thermal ablation for Patients With stage I non-small cell lung cancer

RATIONALE AND OBJECTIVES

The objective of this study was to measure the safety and efficacy of thermal ablation, including radiofrequency ablation (RFA) and microwave ablation (MWA), for patients with stage I non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The databases PubMed was searched from inception to November 2023 to identify relevant studies. Statistical analyses were performed with R version 3. 6. 3.

RESULTS

Thirty-three studies involving 1400 patients were finally included. According to our study, the incidence of patients with stage I NSCLC who were older than 60 years old was 98 % (95 % CI [94-100 %]); the lesions were mostly located in RUL (Right Upper Lobe) and LUL (Left Upper Lobe), and the incidence of the two sites was 29 % (95 % CI [23-35 %]) and 27 % (95 % CI [21-33 %]), respectively; the types of lung cancers mainly included adenocarcinoma, squamous carcinoma, and large-cell lung cancer, of which adenocarcinoma accounted for the largest proportion of 63 % (95 % CI [56-70 %]); the causes of death were mainly categorized into cancer-related (57 %, 95 %CI[40-74 %]) and noncancer-related (40 %, 95 %CI [23-58 %]); the common complications in the postoperative period were pneumothorax and pain, with the incidence of 33 % (95 %CI[24-44 %]) and 33 % (95 %CI[19-50 %]), and the rate of the postoperative complications in MWA was slightly higher than those in RFA; the local recurrence rate was 23 % (95 %CI[17-29 %]) and the distant recurrence rate was 18 % (95 %CI[7-32 %]); the pooling result showed the rate of 1-, 2-, 3-, and 5-year survival rate were 96 %, 81 %, 68 %, and 42 %, the Cancer-specific survival (CSS) rates at 1, 2, 3, and 5 years were 98 %, 88 %, 75 %, and 58 %, Disease-free survival (DFS) rates at 1, 2, 3, and 5 years were 87 %, 63 %, 57 %, and 42 %, there were no significant differences existed between the RFA group and MWA group in survival rate, CSS and DFS.

CONCLUSION

Ablation therapy is safe and effective for stage I NSCLC patient. MWA and RFA have comparable efficacy, safety, and prognosis, which could be recommended for patients with stageⅠNSCLC, especially for patients who cannot tolerate open surgery.

Pre-operative lung ablation prediction using deep learning

OBJECTIVE

Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments.

MATERIALS AND METHODS

Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor's estimates.

RESULTS

The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49-67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes (p = 0.169) unlike the vendor's estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown.

CONCLUSIONS

We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence.

CLINICAL RELEVANCE STATEMENT

Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence.

Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation

PURPOSE

To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA).

METHODS

Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis.

RESULTS

A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality.

CONCLUSIONS

The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.

Retrospective cohort study on the correlation analysis among peri-procedural factors, complications, and local tumor progression of lung tumors treated with CT-guided microwave ablation

Background

Despite adherence to guidelines, recurrence of lesions remains possible in lung tumor microwave ablation (MWA) even when termination is enabled by 5-10 mm ground glass changes. Limited evidence exists regarding the correlation between timely management of perioperative complications (including pneumothorax, pleural effusion, hemorrhage, cavity formation, and infection) and local tumor progression. This retrospective study aimed to investigate the relationship among peri-procedural factors, complications, and local tumor progression in 164 cases of lung tumors treated with computed tomography-guided MWA (CT-MWA), and improve the local prognosis and reduce the complication rate of CT-guided lung tumor ablation.

Methods

We reviewed 164 consecutive patients who underwent CT-MWA at Fudan University Shanghai Cancer Center's Minimally Invasive Therapy Center for lung cancer from September 2019 to May 2020. Correlative analysis was performed between peri-procedural factors, complications and outcomes (local tumor progression rates). Patients who have had prior surgery or previous MWA were excluded. Ablation was the first treatment of choice, and all patients who have had other treatments were excluded. Patients were followed every 3 months with CT. Outcomes of ablation including complications and local tumor progression were evaluated. Peri-procedural factors included demographical factors, tumor features, ablation parameters, management of intra-procedural pneumothorax, and CT features. Complications included pneumothorax, post-procedural refractory infection, and pleural effusion.

Results

The study included 98 males and 68 females, with an average age of 56.1 years. Local tumor progression rate was negatively correlated with intra-procedural management of pneumothorax (R=-0.550, P=0.0003) and Hounsfield unit (HU) difference between HU before and after procedure (R=-0.855, P=0.006), and positively correlated with the average HU value of immediate post-procedural CT at the measurement points (R=0.857, P=0.00002). The correlation analysis results also showed a positive correlation between infection after procedure and pneumothorax (R=0.340, P=0.0001).

Conclusions

A greater difference between HU before and after the procedure or a decrease in CT values immediately after ablation may predict a higher rate of local complete ablation. Prompt management of intraoperative pneumothorax may lower local tumor progression rates and decrease incidence of post-procedural infection.

Complications and local recurrence of malignant liver tumor after ablation in risk areas: a retrospective analysis

INTRODUCTION

Microwave ablation (MWA) is an effective local treatment for malignant liver tumors; however, its efficacy and safety for liver tumors adjacent to important organs are debatable.

PATIENTS AND METHODS

Forty-three cases with liver tumors adjacent to important organs were the risk group and 66 cases were the control group. The complications between two groups were compared by chi-square test and t-test. Local tumor recurrence (LTR) was analyzed by log-rank test. Factors affecting complications were analyzed by logistic regression and Spearman analyses. Factors affecting LTR were analyzed by Cox regression analysis. A receiver operating characteristic curve predicted pain treated with drugs and LTR.

RESULTS

We found no significant difference in complications and LTR between two groups. The risk group experienced lower ablation energy and more antennas per tumor than control group. Necrosis volume after MWA was positively correlated with pain; necrosis volume and ablation time were positively correlated with recovery duration. Major diameter of tumor >3 cm increased risk of LTR by 3.319-fold, good lipiodol deposition decreased risk of LTR by 73.4%. The area under the curve (AUC) for necrosis volume in predicting pain was 0.74, with a 69.1 cm3 cutoff. AUC for major diameter of tumor in predicting LTR was 0.68, with a 27.02 mm cutoff.

CONCLUSION

MWA on liver tumors in at-risk areas is safe and effective, this is largely affected by proper ablation energy, antennas per tumor, and experienced doctors. LTR is primarily determined by major diameter of tumor and lipiodol deposition status.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases

Objectives

To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed.

Methods

This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared.

Results

Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%).

Conclusion

This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.