[18F]FDG PET/CT-based response assessment of stage IV non-small cell lung cancer treated with paclitaxel-carboplatin-bevacizumab with or without nitroglycerin patches

Nitroglycerin: a comprehensive review in cancer therapy

Nitroglycerin (NTG) is a prodrug that has long been used in clinical practice for the treatment of angina pectoris. The biotransformation of NTG and subsequent release of nitric oxide (NO) is responsible for its vasodilatating property. Because of the remarkable ambivalence of NO in cancer disease, either protumorigenic or antitumorigenic (partly dependent on low or high concentrations), harnessing the therapeutic potential of NTG has gain interest to improve standard therapies in oncology. Cancer therapeutic resistance remains the greatest challenge to overcome in order to improve the management of cancer patients. As a NO releasing agent, NTG has been the subject of several preclinical and clinical studies used in combinatorial anticancer therapy. Here, we provide an overview of the use of NTG in cancer therapy in order to foresee new potential therapeutic avenues.

A radiomics prognostic scoring system for predicting progression-free survival in patients with stage IV non-small cell lung cancer treated with platinum-based chemotherapy

Objective

To develop and validate a radiomics prognostic scoring system (RPSS) for prediction of progression-free survival (PFS) in patients with stage IV non-small cell lung cancer (NSCLC) treated with platinum-based chemotherapy.

Methods

In this retrospective study, four independent cohorts of stage IV NSCLC patients treated with platinum-based chemotherapy were included for model construction and validation (Discovery: n=159; Internal validation: n=156; External validation: n=81, Mutation validation: n=64). First, a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography (CT) images of each patient. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator method (LASSO) penalized Cox regression analysis. Finally, an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.

Results

The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts (All P<0.05). On the multivariable analysis, independent factors for PFS were radiomics signature, performance status (PS), and N stage, which were all selected into construction of RPSS. The RPSS showed significant prognostic performance for predicting PFS in discovery [C-index: 0.772, 95% confidence interval (95% CI): 0.765−0.779], internal validation (C-index: 0.738, 95% CI: 0.730−0.746), external validation (C-index: 0.750, 95% CI: 0.734−0.765), and mutation validation (C-index: 0.739, 95% CI: 0.720−0.758). Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness (All P<0.05).

Conclusions

This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stage IV NSCLC patients treated with platinum-based chemotherapy, which holds promise for guiding personalized pre-therapy of stage IV NSCLC.

First-In-Human Study in Cancer Patients Establishing the Feasibility of Oxygen Measurements in Tumors Using Electron Paramagnetic Resonance With the OxyChip

OBJECTIVE

The overall objective of this clinical study was to validate an implantable oxygen sensor, called the 'OxyChip', as a clinically feasible technology that would allow individualized tumor-oxygen assessments in cancer patients prior to and during hypoxia-modification interventions such as hyperoxygen breathing.

METHODS

Patients with any solid tumor at ≤3-cm depth from the skin-surface scheduled to undergo surgical resection (with or without neoadjuvant therapy) were considered eligible for the study. The OxyChip was implanted in the tumor and subsequently removed during standard-of-care surgery. Partial pressure of oxygen (pO2) at the implant location was assessed using electron paramagnetic resonance (EPR) oximetry.

RESULTS

Twenty-three cancer patients underwent OxyChip implantation in their tumors. Six patients received neoadjuvant therapy while the OxyChip was implanted. Median implant duration was 30 days (range 4-128 days). Forty-five successful oxygen measurements were made in 15 patients. Baseline pO2 values were variable with overall median 15.7 mmHg (range 0.6-73.1 mmHg); 33% of the values were below 10 mmHg. After hyperoxygenation, the overall median pO2 was 31.8 mmHg (range 1.5-144.6 mmHg). In 83% of the measurements, there was a statistically significant (p ≤ 0.05) response to hyperoxygenation.

CONCLUSIONS

Measurement of baseline pO2 and response to hyperoxygenation using EPR oximetry with the OxyChip is clinically feasible in a variety of tumor types. Tumor oxygen at baseline differed significantly among patients. Although most tumors responded to a hyperoxygenation intervention, some were non-responders. These data demonstrated the need for individualized assessment of tumor oxygenation in the context of planned hyperoxygenation interventions to optimize clinical outcomes.

The Biological Meaning of Radiomic Features

An earlier incorrect version appeared online. This article was corrected on February 10, 2021.

Response evaluation after immunotherapy in NSCLC: Early response assessment using FDG PET/CT

The present study aimed to evaluate the role of early F-18 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) in non-small cell lung cancer patients undergoing immune checkpoint inhibitor (ICI) treatment.Twenty-four non-small cell lung cancer patients who received nivolumab or pembrolizumab and underwent FDG PET/CT as an interim analysis after 2 or 3 cycles of ICI treatment were retrospectively enrolled. Tumor response was assessed using the PET Response Criteria in Solid Tumors 1.0 (PERCIST) and the European Organization for Research and Treatment of Cancer (EORTC) criteria after 2 or 3 cycles of ICI treatment (SCAN-1) and after an additional 2 cycles of ICI treatment (SCAN-2). The best overall response was determined by FDG PET/CT or chest CT at ≥ 3 months after therapy initiation, and the clinical benefit was investigated. progression-free survival was investigated, and its correlation with clinicopathologic and metabolic parameters was examined using a Cox multivariate proportional hazards model.In the interim analysis, 4 patients achieved a complete metabolic response (CMR), 1 patient exhibited a partial metabolic response (PMR), and 14 patients had Progressive metabolic disease (PMD) according to the PERCIST and EORTC criteria. Four patients showed stable metabolic disease (SMD) according to the PERCIST criteria, and 2 patients showed different responses (i.e., PMR) according to the EORTC criteria. Patients with a CMR or PMR at SCAN-1 had a clinical benefit. Among the 4 patients with SMD at SCAN-1, only 1 experienced a clinical benefit regardless of the percent change in the peak standardized uptake value. Two patients with discordant response assessments between the PERCIST and EORTC criteria showed conflicting clinical benefits. Among the 14 patients with PMD, none experienced any clinical benefit. Only metabolic parameters were significant factors for predicting progression in the multivariate analysis (peak standardized uptake value and metabolic tumor volume, HRs of 1.18 and 1.00, respectively).Based on early F-18 FDG PET/CT after ICI treatment, metabolic parameters could predict post-treatment progression. Responses after ICI treatment were correctly assessed in patients with a CMR, a PMR, and PMD, but patients with SMD required a meticulous follow-up because of varying clinical benefits.

Nitric Oxide Nano-Delivery Systems for Cancer Therapeutics: Advances and Challenges

Nitric oxide (NO) plays important roles in various physiological and pathological functions and processes in the human body. Therapeutic application of NO molecules has been investigated in various diseases, including cardiovascular disease, cancer, and infections. However, the extremely short half-life of NO, which limits its clinical use considerably, along with non-specific distribution, has resulted in a low therapeutic index and undesired adverse effects. To overcome the drawbacks of using this gaseous signaling molecule, researchers in the last several decades have focused on innovative medical technologies, specifically nanoparticle-based drug delivery systems (DDSs), because these systems alter the biodistribution of the therapeutic agent through controlled release at the target tissues, resulting in a significant therapeutic drug effect. Thus, the application of nano-systems for NO delivery in the field of biomedicine, particularly in the development of new drugs for cancer treatment, has been increasing worldwide. In this review, we discuss NO delivery nanoparticle systems, with the aim of improving drug delivery development for conventional chemotherapies and controlling multidrug resistance in cancer treatments.

Application of artificial intelligence in clinical non-small cell lung cancer

Lung cancer is the most common cause of cancer death in the world. Early diagnosis, screening and precise individualized treatment can significantly reduce the death rate of lung cancer. Artificial intelligence (AI) has been shown to be able to help clinicians make more accurate judgments and decisions in many ways. It has been involved in the screening of lung cancer, the judgment of benign and malignant degree of pulmonary nodules, the classification of histological cancer, the differentiation of histological subtypes, the identification of genomics, the judgment of the effectiveness of treatment and even the prognosis. AI has shown that it can be an excellent assistant for clinicians. This paper reviews the application of AI in the field of non-small cell lung cancer and describes the relevant progress. Although most of the studies to evaluate the clinical application of AI in non-small cell lung cancer have not been repeatable and generalizable, the research results highlight the efforts to promote the clinical application of AI technology and influence the future treatment direction.

Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer?

BACKGROUND

Muscle depletion negatively impacts treatment efficacy and survival rates in cancer. Prevention and timely treatment of muscle loss require prediction of patients at risk. We aimed to investigate the potential of skeletal muscle radiomic features to predict future muscle loss.

METHODS

A total of 116 patients with stage IV non-small cell lung cancer included in a randomised controlled trial (NCT01171170) studying the effect of nitroglycerin added to paclitaxel-carboplatin-bevacizumab were enrolled. In this post hoc analysis, muscle cross-sectional area and radiomic features were extracted from computed tomography images obtained before initiation of chemotherapy and shortly after administration of the second cycle. For internal cross-validation, the cohort was randomly split in a training set and validation set 100 times. We used least absolute shrinkage and selection operator method to select features that were most significantly associated with muscle loss and an area under the curve (AUC) for model performance.

RESULTS

Sixty-nine patients (59%) exhibited loss of skeletal muscle. One hundred ninety-three features were used to construct a prediction model for muscle loss. The average AUC was 0.49 (95% confidence interval [CI]: 0.36, 0.62). Differences in intensity and texture radiomic features over time were seen between patients with and without muscle loss.

CONCLUSIONS

The present study shows that skeletal muscle radiomics did not predict future muscle loss during chemotherapy in non-small cell lung cancer. Differences in radiomic features over time might reflect myosteatosis. Future imaging analysis combined with muscle tissue analysis in patients and in experimental models is needed to unravel the biological processes linked to the radiomic features.

The prognostic value of early onset, CT derived loss of muscle and adipose tissue during chemotherapy in metastatic non-small cell lung cancer

OBJECTIVES

To evaluate the relationship between early changes in muscle and adipose tissue during chemotherapy and overall survival (OS) in stage IV non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

In this post-hoc analysis of the first line NVALT12 trial (NCT01171170) in stage IV NSCLC, skeletal muscle (SM), radiation attenuation (RA), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were assessed at the third lumbar level on CT-images obtained before initiation of chemotherapy and shortly after administration of the second cycle. The contribution of changes in different body compartments to overall survival was assessed.

RESULTS

CT scans of 111 patients were included. Analysis of body composition changes between the baseline and the follow-up scan, revealed that overall SM cross sectional area (CSA), radiation attenuation and SAT CSA decreased respectively by -1.2 ± 2.9 cm²/m² (p < 0.001), -0.7 ± 3.3 HU (p = 0.026) and -1.9 ± 8.7 cm²/m² (p = 0.026), while no significant changes in VAT tissue were observed. Longitudinally, median OS was significantly shorter among patients losing SM compared to patients with preserved SM (9.4 versus 14.2 months; HR 1.9, 95% CI: 1.23, 2.79, p = 0.003). Multivariate analyses showed that proportional loss of muscle mass was associated with poor OS (HR 0.949, 95% CI: 0.915, 0.985, p = 0.006) independent from important clinical prognostic factors including WHO-PS, gender, age and Charlson comorbidity index.

CONCLUSION

Early loss of SM during first line chemotherapy is a poor prognostic factor in stage IV NSCLC patients. Future studies have to reveal whether early supportive intervention guided by initial CT muscle response to chemotherapy can influence the wasting process and related mortality risk.

The interplay of circulating tumor DNA and chromatin modification, therapeutic resistance, and metastasis

Peripheral circulating free DNA (cfDNA) is DNA that is detected in plasma or serum fluid with a cell-free status. For cancer patients, cfDNA not only originates from apoptotic cells but also from necrotic tumor cells and disseminated tumor cells that have escaped into the blood during epithelial-mesenchymal transition. Additionally, cfDNA derived from tumors, also known as circulating tumor DNA (ctDNA), carries tumor-associated genetic and epigenetic changes in cancer patients, which makes ctDNA a potential biomarker for the early diagnosis of tumors, monitory and therapeutic evaluations, and prognostic assessments, among others, for various kinds of cancer. Moreover, analyses of cfDNA chromatin modifications can reflect the heterogeneity of tumors and have potential for predicting tumor drug resistance.

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Predictive value of positron emission tomography for the prognosis of molecularly targeted therapy in solid tumors

Objective

This study aimed at comprehensively exploring the value applying positron emission tomography (PET) to predict the effect of molecularly targeted therapy in solid tumors.

Materials and methods

A systematic search was performed for potentially relevant studies from the time of inception to February 2017. The primary endpoints were progression-free survival (PFS), overall survival (OS), and time to progression (TTP). The results were analyzed by Review Manager version 5.3 (RevMan 5.3) statistical software. Subgroup analyses were implemented based on the type of molecularly targeted agents (monoclonal antibodies arm and small molecular targeted agents arm), mechanism (erlotinib/gefitinib arm and bevacizumab arm), radioactive tracers, type of tumor, and reevaluated PET timing.

Results

Twenty-six studies incorporating 865 individuals were eligible. Compared with PET nonresponse group, PET response group displayed a decrease in maximal standard uptake value (SUVmax), which was associated with a significantly prolonged PFS (HR =0.41, 95% CI [0.29, 0.59]; P<0.00001), OS (HR =0.52, 95% CI [0.40, 0.67]; P<0.00001), and TTP (HR =0.30, 95% CI [0.14, 0.66]; P=0.003). Similar results were obtained in the subgroup analyses of PFS in erlotinib/gefitinib arm and small molecular targeted agents arm; and OS in lung cancer arm, erlotinib/gefitinib arm, bevacizumab arm, small molecular targeted agents arm, monoclonal antibodies arm, 18F-fluorodeoxythymidine (18F-FLT) arm, 18F-fluorodeoxyglucose (18F-FDG) arm, and early PET timing arm.

Conclusion

Our study demonstrated that PET was a favorable approach to predict the prognosis of molecularly targeted therapy for solid tumors. PET assessment within 2 weeks could be useful to predict clinical outcome.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

Quality assessment of positron emission tomography scans: recommendations for future multicentre trials

BACKGROUND

Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET.

MATERIAL AND METHODS

Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters.

RESULTS

From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times.

CONCLUSIONS

Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.