High metabolic tumor volume (MTV) predicts worse outcomes in lymphoma treated with chemotherapy. However, it is unknown if this holds for patients treated with axicabtagene ciloleucel (axi-cel), an anti-CD19 targeted chimeric antigen receptor T-cell therapy. The primary objective of this retrospective study was to investigate the relationship between MTV and survival (overall survival [OS] and progression-free survival [PFS]) in patients with relapsed/refractory large B-cell lymphoma (LBCL) treated with axi-cel. Secondary objectives included finding the association of MTV with response rates and toxicity. The MTV values on baseline positron emission tomography of 96 patients were calculated via manual methodology using commercial software. Based on a median MTV cutoff value of 147.5 mL in the first cohort (n = 48), patients were divided into high and low MTV groups. Median follow-up for survivors was 24.98 months (range, 10.59-51.02 months). Patients with low MTV had significantly superior OS (hazard ratio [HR], 0.25; 95% confidence interval [CI], 0.10-0.66) and PFS (HR, 0.40; 95% CI, 0.18-0.89). Results were successfully validated in a second cohort of 48 patients with a median follow-up for survivors of 12.03 months (range, 0.89-25.74 months). Patients with low MTV were found to have superior OS (HR, 0.14; 95% CI, 0.05-0.42) and PFS (HR, 0.29; 95% CI, 0.12-0.69). In conclusion, baseline MTV is associated with OS and PFS in axi-cel recipients with LBCL.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2–4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091–0.156; P < 2 × 10−16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

The National Lung Screening Trial (NLST) demonstrated that screening with low-dose computed tomography (LDCT) is associated with a 20% reduction in lung cancer mortality. One potential limitation of LDCT screening is overdiagnosis of slow growing and indolent cancers. In this study, peritumoral and intratumoral radiomics was used to identify a vulnerable subset of lung patients associated with poor survival outcomes. Incident lung cancer patients from the NLST were split into training and test cohorts and an external cohort of non-screen detected adenocarcinomas was used for further validation. After removing redundant and non-reproducible radiomics features, backward elimination analyses identified a single model which was subjected to Classification and Regression Tree to stratify patients into three risk-groups based on two radiomics features (NGTDM Busyness and Statistical Root Mean Square [RMS]). The final model was validated in the test cohort and the cohort of non-screen detected adenocarcinomas. Using a radio-genomics dataset, Statistical RMS was significantly associated with FOXF2 gene by both correlation and two-group analyses. Our rigorous approach generated a novel radiomics model that identified a vulnerable high-risk group of early stage patients associated with poor outcomes. These patients may require aggressive follow-up and/or adjuvant therapy to mitigate their poor outcomes.

Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.

We have identified a set of informative and relevant radiologic features that can be easily scored in the clinical setting to determine lung cancer risk among participants with small pulmonary nodules. PurposeTo extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer ris...