[Advances and Challenges of Local Thermal Ablation in Non-small Cell Lung Cancer]

CT-guided Percutaneous Microwave Ablation Combined with Local Radiotherapy or Chemotherapy of Malignant Pulmonary Tumors

BACKGROUND AND OBJECTIVE

The study aimed to investigate the clinical efficacy of CT-guided microwave ablation (MWA) combined with 125I seed implantation or bronchial arterial infusion (BAI) chemotherapy in the treatment of malignant pulmonary tumors.

METHODS

A total of 56 patients who underwent MWA, MWA combined with 125I particle implantation, or MWA combined with BAI chemotherapy for advanced lung cancer or metastatic lung cancer from January 2015 to June 2021 in Guangdong Provincial People's Hospital were enrolled. Among them, 21 patients were treated with MWA (MWA), 18 with MWA combined with 125I seed implantation (MWA+125I), and 17 with MWA combined with BAI chemotherapy (MWA+BAI). The short-term outcomes, complications, Eastern Cooperative Oncology Group (ECOG) performance score (Zubrod-ECOG-WHO, ZPS), survival, and factors related to survival were compared between the three groups.

RESULTS

The response rate of the MWA group (9.52%) was significantly lower than that of the MWA+125I group (50.00%) and MWA+BAI chemotherapy group (47.06%), and the differences were statistically significant (p < 0.05). The incidence of complications in the MWA, MWA+125I, and MWA+BAI chemotherapy groups was 47.62%, 55.56%, and 52.94%, respectively, with no significant difference (p > 0.05). Three months after the treatment, the ZPS of the MWA+125I and MWA+BAI chemotherapy groups was significantly lower than before treatment and significantly lower than that of the MWA group in the same period; the differences were statistically significant (p < 0.05). The median survival time of the MWA+125I group was 18 (9.983, 26.017) months and that of the MWA+BAI chemotherapy group was 21 (0.465, 41.535) months, both of which were higher than that of the MWA group [11 (6.686, 15.314) months]; the differences were statistically significant (p < 0.05). Cox regression analysis was performed on the factors related to survival and revealed treatment mode as a protective factor [HR = 0.433, 95% CI = (0.191, 0.984), p = 0.046]. Other factors, such as gender, age, and tumor size, did not independently affect survival.

CONCLUSION

CT-guided MWA combined with 125I seed implantation and MWA combined with BAI chemotherapy are safe and effective for the treatment of advanced lung cancer and metastatic lung cancer, and can control tumor progression and prolong survival time.

Hyperthermia combined with immune checkpoint inhibitor therapy in the treatment of primary and metastatic tumors

According to the difference in temperature, thermotherapy can be divided into thermal ablation and mild hyperthermia. The main advantage of thermal ablation is that it can efficiently target tumors in situ, while mild hyperthermia has a good inhibitory effect on distant metastasis. There are some similarities and differences between the two therapies with respect to inducing anti-tumor immune responses, but neither of them results in sustained systemic immunity. Malignant tumors (such as breast cancer, pancreatic cancer, nasopharyngeal carcinoma, and brain cancer) are recurrent, highly metastatic, and highly invasive even after treatment, hence a single therapy rarely resolves the clinical issues. A more effective and comprehensive treatment strategy using a combination of hyperthermia and immune checkpoint inhibitor (ICI) therapies has gained attention. This paper summarizes the relevant preclinical and clinical studies on hyperthermia combined with ICI therapies and compares the efficacy of two types of hyperthermia combined with ICIs, in order to provide a better treatment for the recurrence and metastasis of clinically malignant tumors.

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.