Purpose: A phase I study was conducted to determine safety, clinical efficacy, and antitumor immune responses in patients with advanced non–small cell lung carcinoma (NSCLC) following intratumoral administration of autologous dendritic cells (DC) transduced with an adenoviral (Ad) vector expressing the CCL21 gene (Ad-CCL21-DC). We evaluated safety and tumor antigen–specific immune responses following in situ vaccination (ClinicalTrials.gov: NCT01574222).Experimental Design: Sixteen stage IIIB/IV NSCLC subjects received two vaccinations (1 × 106, 5 × 106, 1 × 107, or 3 × 107 DCs/injection) by CT- or bronchoscopic-guided intratumoral injections (days 0 and 7). Immune responses were assessed by tumor antigen–specific peripheral blood lymphocyte induction of IFNγ in ELISPOT assays. Tumor biopsies were evaluated for CD8+ T cells by IHC and for PD-L1 expression by IHC and real-time PCR (RT-PCR).Results: Twenty-five percent (4/16) of patients had stable disease at day 56. Median survival was 3.9 months. ELISPOT assays revealed 6 of 16 patients had systemic responses against tumor-associated antigens (TAA). Tumor CD8+ T-cell infiltration was induced in 54% of subjects (7/13; 3.4-fold average increase in the number of CD8+ T cells per mm2). Patients with increased CD8+ T cells following vaccination showed significantly increased PD-L1 mRNA expression.Conclusions: Intratumoral vaccination with Ad-CCL21-DC resulted in (i) induction of systemic tumor antigen–specific immune responses; (ii) enhanced tumor CD8+ T-cell infiltration; and (iii) increased tumor PD-L1 expression. Future studies will evaluate the role of combination therapies with PD-1/PD-L1 checkpoint inhibition combined with DC-CCL21 in situ vaccination. Clin Cancer Res; 23(16); 4556–68. ©2017 AACR.

To assess whether percutaneous radiofrequency (RF) ablation of unresectable pulmonary malignancies is safe and technically feasible and to evaluate the usefulness of computed tomographic (CT) nodule densitometry as a tool for following up tumors after ablation. Twelve patients (seven men and five women; mean age, 60.6 years) with unresectable disease (because of poor lung reserve or multifocality) underwent nodule CT densitometry and CT-guided percutaneous RF ablation of 19 lung tumors (six [32%] tumors were adenocarcinoma, one (5%) was large cell carcinoma, two (10%) were bronchoalveolar carcinoma, four (21%) were colorectal carcinoma, and six (32%) were sarcoma less than 50 cm2 in area (range, 0.25-35.00 cm2). No patients had symptoms of their disease before RF ablation. Follow-up CT densitometry was scheduled for 1, 3, 6, and 12 months after RF ablation. Lesions were evaluated for change in area and contrast enhancement at follow-up CT. RF ablation was well tolerated by all patients. Intraprocedural complications included 12 cases of pneumothoraces (two patients required chest tube placement, while 10 were asymptomatic and required no further treatment), two cases of pleural effusion, and two cases of moderate pain (one case during and one case both during and after the procedure). Mean follow-up was 4(1/2) months (range, 1-12 months). In the eight patients with 3-month follow-up, lesion size increased in two and remained stable in six. Mean contrast enhancement, however, decreased from 46.8 HU (range, 19-107 HU) at baseline to 9.6 HU (range, 0-32 HU) at 1-2-month follow-up. In the one patient with 12-month CT densitometry follow-up, lesion enhancement was less than 50% of that at baseline, and lesion diameter remained stable. These preliminary results show that percutaneous RF ablation is a safe and technically feasible management option for unresectable pulmonary malignancies. CT densitometry may have potential for future use as a noninvasive method of following up tumors after RF ablation.

Percutaneous image-guided thermal ablation is gaining attraction as an effective alternative to surgical resection for patients with primary and secondary malignancies of the lung. Currently, no standard follow-up imaging protocol has been established or uniformly accepted. The early identification of residual or recurrent tumor would in theory enable the practitioner to offer expeditious retreatment or alternative treatment. This review elaborates on the imaging findings following thermal ablation, both heat- and cold-based, of nonresectable pulmonary malignancies.

Chest 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. A 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. Fifty-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. Chest 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.

Interventional oncology and management of thoracic malignancies with ablative techniques are becoming ever more recognized therapeutic options. With increased understanding, development, and utility of the ablative techniques, the indications are expanding and efficacy improving. Lung cancer was among the first indications for lung ablation and remains most challenging with multiple therapeutic options. For inoperable patients, the current literature demonstrates equivalent survivals between ablation, sublobar resection, and stereotactic body radiation. Oligometastatic disease remains the most common indication for lung ablation and is gaining acceptance among the oncology community, as lung ablation provides limited patient downtime, repeatability, and minimal to no loss of respiratory function. Other indications for ablation are being explored, including recurrent mesothelioma, drop metastasis from thymoma, and limited pleural metastasis, with excellent local control of tumor and limited complications. Follow-up after ablation is essential to detect early complications, observe the natural evolution of the ablation zone, and detect recurrence. Standardized imaging follow-up allows for these goals to be achieved and provides a framework for oncology practice. In this article, the role of ablation in the management of thoracic neoplasms and postablation imaging features are reviewed. The radiologists, in particular, thoracic radiologists should be able to identify candidates who can benefit from ablation familiarize themselves with postablation imaging features, and recognize the evolution of the postablation zone and hence detect early recurrence.