Antibody-targeting of ultra-small nanoparticles enhances imaging sensitivity and enables longitudinal tracking of multiple myeloma.

Monitoring malignant progression and disease recurrence post-therapy are central challenges to improving the outcomes of patients with multiple myeloma (MM). Whereas current detection methods that rely upon bone marrow examination allow for precise monitoring of minimal residual disease and can help to elucidate clonal evolution, they do not take into account the spatial heterogeneity of the tumor microenvironment. As such, they are uninformative as to the localization of malignant plasma cells and may lead to false negative results.

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

Background: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

Methods And Findings: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.

Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC.

Purpose: Radiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC).

Radiographic prediction of meningioma grade by semantic and radiomic features.

Objectives: The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.

Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics.

Lymph node volume predicts survival but not nodal clearance in Stage IIIA-IIIB NSCLC.

Background: Locally advanced non-small cell lung cancer (LA-NSCLC) patients have poorer survival and local control with mediastinal node (N2) tumor involvement at resection. Earlier assessment of nodal burden could inform clinical decision-making prior to surgery. This study evaluated the association between clinical outcomes and lymph node volume before and after neoadjuvant therapy.

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT.

Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed.

Radiologic-pathologic correlation of response to chemoradiation in resectable locally advanced NSCLC.

Objectives: Accurate assessment of tumor response to chemoradiation has the potential to guide clinical decision-making regarding surgical resection and/or dose escalation for patients. Early assessment has implications for Optimal local therapy for operable locally advanced non-small cell lung cancer (LA-NSCLC) is controversial. This study evaluated quantitative CT-based tumor measurements to predict pathologic response.

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

Introduction: Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision making. We investigated the value of radiomic data extracted from pretreatment computed tomography images of the primary tumor and lymph nodes in predicting pathological response after neoadjuvant chemoradiation before surgery.

Methods: A total of 85 patients with resectable locally advanced (stage II-III) NSCLC (median age 60.

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer.

PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic F-FDG PET scans and were tested for genetic mutations.

CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer.

Background: Radiomics uses a large number of quantitative imaging features that describe the tumor phenotype to develop imaging biomarkers for clinical outcomes. Radiomic analysis of pre-treatment computed-tomography (CT) scans was investigated to identify imaging predictors of clinical outcomes in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

Materials And Methods: CT images of 113 stage I-II NSCLC patients treated with SBRT were analyzed.

Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients.

Purpose: Although change in standardized uptake value (SUV) measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer.

Methods: Fifty-four esophageal cancer patients received PET/CT scans before and after chemoradiotherapy.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction.

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer.

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

Background And Purpose: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.

Material And Methods: We included two datasets: 98 patients for discovery and 84 for validation.

Low incidence of chest wall pain with a risk-adapted lung stereotactic body radiation therapy approach using three or five fractions based on chest wall dosimetry.

Purpose: To examine the frequency and potential of dose-volume predictors for chest wall (CW) toxicity (pain and/or rib fracture) for patients receiving lung stereotactic body radiotherapy (SBRT) using treatment planning methods to minimize CW dose and a risk-adapted fractionation scheme.

Methods: We reviewed data from 72 treatment plans, from 69 lung SBRT patients with at least one year of follow-up or CW toxicity, who were treated at our center between 2010 and 2013. Treatment plans were optimized to reduce CW dose and patients received a risk-adapted fractionation of 18 Gy×3 fractions (54 Gy total) if the CW V30 was less than 30 mL or 10-12 Gy×5 fractions (50-60 Gy total) otherwise.