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Background: Percutaneous Microwave Ablation (MWA) of lung malignancies is a procedure with many technical challenges, among them the risk of residual disease. Recently, dedicated software able to predict the volume of the ablated area was introduced. Cone-beam computed tomography (CBCT) is the imaging guidance of choice for pulmonary ablation in our institution. The volumetric prediction software (VPS) has been installed and used in combination with CBCT to check the correct position of the device. Our study aimed to compare the results of MWA of pulmonary tumours performed using CBCT with and without VPS. Methods: We retrospectively reviewed 1-month follow-up enhanced contrast-enhanced computed tomography (CECT) scans of 10 patients who underwent ablation with the assistance of VPS (group 1) and of 10 patients who were treated without the assistance of VPS (group 2). All patients were treated for curative purposes, the maximum axial diameter of lesions ranged between 5 and 22 mm in group 1 and between 5 and 25 mm in group 2. We compared the presence of residual disease between the two groups. Results: In group 1 residual disease was seen in only 1 patient (10%) in which VPS had ensured complete coverage of the tumour. In group 2 residual disease was found in 3 patients (30%). Conclusions: Using this software during MWA of lung malignancies could improve the efficacy of the treatment compared to the conventional only CBCT guidance.

Imaging-guided percutaneous ablative treatments, such as radiofrequency ablation (RFA), cryoablation and microwave ablation (MWA), have been developed for the treatment of unresectable primary and secondary lung tumors in patients with advanced-stage disease or comorbidities contraindicating surgery. Among these therapies, MWA has recently shown promising results in the treatment of pulmonary neoplasms. The potential advantages of MWA over RFA include faster ablation times, higher intra-tumoral temperatures, larger ablation zones and lower susceptibility to the heat sink effect, resulting in greater efficacy in proximity to vascular structures. Despite encouraging results supporting its efficacy, there is a relative paucity of data in the literature regarding the role of computer tomography (CT) to monitor MWA-treated lesions, and the CT appearance of their morphologic evolution and complications. For both interventional and non-interventional radiologists, it is crucial to be familiar with the CT features of such treated lesions in order to detect incomplete therapy or recurrent disease at early stage, as well as to recognize initial signs of complications. The aim of this pictorial essay is to describe the typical CT features during follow-up of lung lesions treated with percutaneous MWA and how to interpret and differentiate them from other radiological findings, such as recurrence and complications, that are commonly encountered in this setting.

PURPOSEChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.METHODSA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student’s t-test or Mann-Whitney U test. Cohen’s kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.RESULTSFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.CONCLUSIONChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.