State of the art: Response assessment in lung cancer in the era of genomic medicine

Combined use of CYFRA 21-1 and CA 125 predicts survival of patients with metastatic NSCLC and stable disease in IMpower150

BACKGROUND

Patients with non-small cell lung cancer (NSCLC) and stable disease (SD) have an unmet clinical need to help guide early treatment adjustments.

OBJECTIVE

To evaluate the potential of tumor biomarkers to inform on survival outcomes in NSCLC SD patients.

METHODS

This post hoc analysis included 480 patients from the IMpower150 study with metastatic NSCLC, treated with chemotherapy, atezolizumab and bevacizumab combinations, who had SD at first CT scan (post-treatment initiation). Patients were stratified into high- and low-risk groups (overall survival [OS] and progression-free survival [PFS] outcomes) based on serum tumor biomarker levels.

RESULTS

The CYFRA 21-1 and CA 125 biomarker combination predicted OS and PFS in patients with SD. Risk of death was ~4-fold higher for the biomarker-stratified high-risk versus low-risk SD patients (hazard ratio [HR] 3.80; 95% confidence interval [CI] 3.02-4.78; p < 0.0001). OS in patients with the low- and high-risk SD was comparable to that in patients with the CT-defined partial response (PR; HR 1.10; 95% CI 0.898-1.34) and progressive disease (PD) (HR 1.05; 95% CI 0.621-1.77), respectively. The findings were similar with PFS, and consistent across treatment arms.

CONCLUSIONS

Biomarker testing shows potential for providing prognostic information to help direct treatment in NSCLC patients with SD. Prospective clinical studies are warranted.ClinicalTrials.gov: NCT02366143.

Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

Cancer immunotherapy efficacy and machine learning

INTRODUCTION

Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.

AREAS COVERED

Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).

EXPERT OPINION

An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.

Imaging Follow-Up of Nonsurgical Therapies for Lung Cancer: AJR Expert Panel Narrative Review

Lung cancer continues to be the most common cause of cancer-related death worldwide. In the past decade, with the implementation of lung cancer screening programs and advances in surgical and nonsurgical therapies, the survival of patients with lung cancer has increased, as has the number of imaging studies that these patients undergo. However, most patients with lung cancer do not undergo surgical re-section, because they have comorbid disease or lung cancer in an advanced stage at diagnosis. Nonsurgical therapies have continued to evolve with a growing range of systemic and targeted therapies, and there has been an associated evolution in the imaging findings encountered at follow-up examinations after such therapies (e.g., with respect to posttreatment changes, treatment complications, and recurrent tumor). This AJR Expert Panel Narrative Review describes the current status of nonsurgical therapies for lung cancer and their expected and unexpected imaging manifestations. The goal is to provide guidance to radiologists regarding imaging assessment after such therapies, focusing mainly on non-small cell lung cancer. Covered therapies include systemic therapy (conventional chemotherapy, targeted therapy, and immunotherapy), radiotherapy, and thermal ablation.

Prediction Model for Tumor Volume Nadir in EGFR -mutant NSCLC Patients Treated With EGFR Tyrosine Kinase Inhibitors

PURPOSE

In patients with advanced non-small cell lung cancer (NSCLC) and oncogenic driver mutations treated with effective targeted therapy, a characteristic pattern of tumor volume dynamics with an initial regression, nadir, and subsequent regrowth is observed on serial computed tomography (CT) scans. We developed and validated a linear model to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR tyrosine kinase inhibitors (TKI).

MATERIALS AND METHODS

Patients with EGFR -mutant advanced NSCLC treated with EGFR-TKI as their first EGFR-directed therapy were studied for CT tumor volume kinetics during therapy, using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir in a training cohort of 34 patients, and then was validated in an independent cohort of 84 patients.

RESULTS

The linear model for tumor nadir prediction was obtained in the training cohort of 34 patients, which utilizes the baseline tumor volume before initiating therapy (V 0 ) to predict the volume decrease (mm 3 ) when the nadir volume (V p ) was reached: V 0 -V p =0.717×V 0 -1347 ( P =2×10 -16 ; R2 =0.916). The model was tested in the validation cohort, resulting in the R2 value of 0.953, indicating that the prediction model generalizes well to another cohort of EGFR -mutant patients treated with EGFR-TKI. Clinical variables were not significant predictors of tumor volume nadir.

CONCLUSION

The linear model was built to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR-TKIs, which provide an important metrics in treatment monitoring and therapeutic decisions at nadir such as additional local abrasive therapy.

Correlation of Short Leukocyte Telomeres and Oxidative Stress with the Presence and Severity of Lung Cancer Explored by Principal Component Analysis

Lung cancer (LC) is the second most common malignancy and leading cause of cancer death. The potential "culprit" for local and systemic telomere shortening in LC patients is oxidative stress. We investigated the correlation between the peripheral blood leukocyte (PBL) telomere length (TL) and the presence/severity of LC and oxidative stress, and its usefulness as LC diagnostic marker. PBL TL was measured in 89 LC patients and 83 healthy subjects using the modified Cawthon RTq-PCR method. The relative PBL TL, found to be a potential diagnostic marker for LC with very good accuracy (P < 0.001), was significantly shorter in patients compared to the control group (CG) (P < 0.001). Significantly shorter telomeres were found in patients with LC TNM stage IV than in patients with stages I-III (P = 0.014), in patients without therapy compared to those on therapy (P = 0.008), and in patients with partial response and stable/progressive disease compared to those with complete response (P = 0.039). The total oxidant status (TOS), advanced oxidation protein products (AOPP), prooxidant-antioxidant balance (PAB) and C-reactive protein (CRP) were significantly higher in patients compared to CG (P < 0.001) and correlated negatively with TL in both patients and CG (P < 0.001). PCA showed a relation between PAB and TL, and between the EGFR status and TL. Oxidative stress and PBL telomere shortening are probably associated with LC development and progression.

Safety and Efficacy of Dostarlimab in Patients With Recurrent/Advanced Non-small Cell Lung Cancer: Results from Cohort E of the Phase I GARNET Trial

BACKGROUND

Dostarlimab is an anti-programmed cell death protein-1 antibody being evaluated in recurrent/advanced solid tumors, including non-small cell lung cancer (NSCLC), in the ongoing Phase I, multi-center, open-label, 2-part (dose escalation and cohort expansion) GARNET study (NCT02715284).

MATERIALS AND METHODS

Here, we report an interim analysis of patients with recurrent/advanced NSCLC who progressed following platinum-based chemotherapy. Patients received dostarlimab (500 mg IV every 3 weeks [Q3W] for Cycles 1-4, then 1000 mg Q6W) until disease progression or unacceptable toxicity for > 2 years. The primary endpoints were immune-related objective response rate (irORR) per investigator-assessed irRECIST and safety.

RESULTS

As of 8, July 2019, 67 patients with recurrent/advanced NSCLC were enrolled and treated with dostarlimab; the majority had programmed death ligand 1 (PD-L1) tumor proportion score (TPS) < 1% (35.8% of patients) or PD-L1 TPS 1%-49% (29.9% of patients); 7.5% had PD-L1 TPS ≥ 50%, and 26.9% had unknown PD-L1 TPS status. Median follow-up was 13.8 months (range: 0.0-22.6). irORR was 26.9%, including 2 complete and 16 partial responses. The median duration of response of 11.6 months (range: 2.8-19.4). Responses were observed in 2 of 24 (16.7%) patients with PD-L1 TPS < 1%, 4 of 20 (20.0%) patients with PD-L1 TPS 1%-49% and 2 of 5 (40.0%) patients with PD-L1 TPS ≥ 50%. Fatigue (4.5%) was the most common Grade ≥ 3 treatment-related treatment-emergent adverse event (TRAE). Immune-related TRAEs (any grade) were observed in 28.4% of patients.

CONCLUSION

Dostarlimab demonstrated promising antitumor activity in advanced/recurrent NSCLC that progressed following platinum-based chemotherapy, including across all PD-L1 subgroups, and has an acceptable safety profile.

Survival Analysis and Clinical Outcomes between Paclitaxel and Carboplatin Versus Carboplatin and Gemcitabine in Patients with Advanced-stage Non-small-cell Lung Cancer: A Single-center Cohort Study

BACKGROUND

Palliative chemotherapy using platinum-based doublet chemotherapy was recommended as one of the standard treatments in patients with advanced-stage non-small cell lung cancer (NSCLC) with negative EGFR mutation. This study aimed to compare clinical outcomes between patients treated with paclitaxel and carboplatin (PC) and those treated with carboplatin and gemcitabine (CG).

METHODS

We conducted a retrospective cohort study comparing PC and CG at Hatyai Hospital between 2012 and 2019. The primary outcome was survival analysis, and the secondary outcome was chemotherapy-related adverse events, and the rate and reason for stopping chemotherapy.

RESULT

The median overall survivals of both groups was comparable (9.0 months for the PC group and 9.6 months for the CG group; log-rank, p=0.287). The CG group had a higher incidence of adverse events (89.7% vs. 77.9%, p=0.010) and tended to have a lower rate of chemotherapy discontinuation (29.6% vs. 41.2%, p=0.080) than the PC group. In the multivariate analysis, female sex (odds ratio [OR]=0.351; 95% confidence interval [CI], 0.158-0.780; p=0.010) and higher performance status (OR=76.374; 95%CI, 32.533-179.295; p<0.001) were independent predictive factors for stopping chemotherapy. In the proportional hazards model, the factors associated with decreased survival included higher performance status (hazard ratio [HR]=1.939; 95%CI, 1.388-2.709; P<0.001) and discontinuation of chemotherapy (HR=2.572; 95%CI, 1.792-3.691; p=<0.001).

CONCLUSION

These two platinum-based regimens had comparable effects on overall survival. The CG group had a higher incidence of chemotherapy-related adverse events, while the PC group had a marginally significantly higher rate of stopping chemotherapy from unacceptable adverse events and deterioration of patients' clinical status.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists’ skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography

Simple Summary

The five-year survival rate of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases, is only 10–20%. A reliable prediction model of overall survival (OS) that integrates imaging and clinical data is required. Overall, 492 patients with NSCLC from two hospitals were enrolled in this study. The compensation method was applied to reduce the variation of imaging features among different hospitals. We constructed a deep learning prediction model, DeepSurv, based on computed tomography radiomics and key clinical features to generate a personalized survival curve for each patient. The results of DeepSurv showed a good performance in discriminating high and low risk of survival. Furthermore, the generated personalized survival curves could be intuitively applied for individual OS prediction in clinical practice. We concluded that the proposed prediction model could benefit physicians, patients, and caregivers in managing NSCLC and facilitate personalized medicine.

Abstract

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.

OUP accepted manuscript

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.

Does CCL19 act as a double-edged sword in cancer development?

Cancer is considered a life-threatening disease, and several factors are involved in its development. Chemokines are small proteins that physiologically exert pivotal roles in lymphoid and non-lymphoid tissues. The imbalance or dysregulation of chemokines has contributed to the development of several diseases, especially cancer. CCL19 is one of the homeostatic chemokines that is abundantly expressed in the thymus and lymph nodes. This chemokine, which primarily regulates immune cell trafficking, is involved in cancer development. Through the induction of anti-tumor immune responses and inhibition of angiogenesis, CCL19 exerts tumor-suppressive functions. In contrast, CCL19 also acts as a tumor-supportive factor by inducing inflammation, cell growth, and metastasis. Moreover, CCL19 dysregulation in several cancers, including colorectal, breast, pancreatic, and lung cancers, has been considered a tumor biomarker for diagnosis and prognosis. Using CCL19-based therapeutic approaches has also been proposed to overcome cancer development. This review will shed more light on the multifarious function of CCL19 in cancer and elucidate its application in diagnosis, prognosis, and even therapy. It is expected that the study of CCL19 in cancer might be promising to broaden our knowledge of cancer development and might introduce novel approaches in cancer management.

Role of PET/CT in post-therapeutic assessment of bronchogenic carcinoma

Background

Lung cancer is the most common among all kinds of cancers. It still constitutes the leading cause of cancer-related deaths worldwide, even with major advancements in prevention and treatments available. More than 85% of the cases are of non-small cell lung cancer (NSCLC), while less than 15% are of small cell lung cancers (SCLCs).

Patients and methods

This is a prospective study of 20 patients confirmed histopathologically to have bronchogenic carcinoma, who came for assessment of therapeutic response. All patients underwent positron emission tomography/computed tomography (PET/CT) before and after therapy. Semiquantitative assessment was used to determine maximum standardized uptake value (SUVmax). Treatment response evaluation was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria.

Results

Comparison of the pre- and post-treatment SUVmax in the responder and non-responder groups revealed that the post-treatment SUV was significantly lower than the baseline SUV in the responder group (P = 0.008). The responder post-treatment SUV and ∆ SUV were significantly lower than the non-responder values (P = 0.014 and 0.0004 respectively). The optimum threshold values of post-treatment SUV and ∆ SUV threshold defined by the receiver operating characteristic (ROC) curve analysis were ≤ 8 and ≤ −48.3 respectively. The sensitivity, specificity, PPV, NPV, and AUC of post-treatment SUV for predicting tumor response were 100%, 66.67%, 66.7%, 100%, and 0.833 respectively. The sensitivity, specificity, PPV, NPV, and AUC of ∆ SUV for predicting tumor response were 100%, 91.67%, 88.9%, 100%, and 0.979% respectively.

Conclusion

PET/CT proved itself as useful, efficient, and reliable tool in follow-up of lung cancer patients as it gives an early and accurate metabolic response assessment before any CT changes, leading to early modification of therapy or confirmation of its efficiency.

Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review

Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.

Prognostic Impact of the Advanced Lung Cancer Inflammation Index (ALI) in Metastatic Non-Small Cell Lung Cancer Treated with First Line Chemotherapy

Background:

The advanced lung cancer inflammation index (ALI) has been reported to predict the overall survival in patients with advanced non-small cell lung cancer (NSCLC). However, no previous studies have examined the prognostic significance of ALI in metastatic NSCLC treated with first line chemotherapy. The objective of this study was to explore the relationship between ALI and the prognosis of metastatic NSCLC treated with first line chemotherapy.

Materials and Methods:

Data of 109 metastatic NSCLC patients who had completed first line treatment with chemotherapy was collected. A multivariate flexible parametric proportional-hazards model with restricted cubic splines (RCS) was used to explore and identify the independent prognostic factors, including clinical potential factors and ALI for the overall survival. Multivariate regression analysis was used to evaluate the potential prognostic factors associated with short survival less than 6 months. The analysis of the restricted mean survival time (RMST) method was used to estimate the event-free time from zero to 18 months.

Results:

The median OS was 10.9 months (95%CI 9.57-13.18) and median PFS was 7.5 months (95%CI 6.85-8.00).The multivariate survival analyses revealed two prognostic factors for worse survival: Poor ECOG PS (HR46.90; 95%CI 2.90-758.73; p=0.007) and progressive disease after completing the first line chemotherapy treatment (HR 2.85; 95%CI1.18-6.88; p=0.02),whereas a low ALI <11 referred to a non-significant prognostic factor (HR 1.42; 95%CI 0.67-3.01; p=0.364).The results of the multivariate regression analysis revealed that the low ALI and progressive disease status were significantly associated with the short survival outcome (OR 5.12; 95%CI 1.11-23.65; p=0.037; OR 12.57; 95%CI 3.00-52.73; p=0.001).

Conclusions:

A low ALI was associated with the short survival in metastatic NSCLC treated with chemotherapy. However, using ALI as a prognostic factor only was still too limited. Other considerable clinical prognostic factors should also be used simultaneously, which would have strong significant prognostic impacts.

Tumor Volume Analysis as a Predictive Marker for Prolonged Survival in Anaplastic Lymphoma Kinase-rearranged Advanced Non-Small Cell Lung Cancer Patients Treated With Crizotinib

PURPOSE

Targeted inhibition of anaplastic lymphoma kinase (ALK) has been widely used for the treatment of advanced non-small cell lung cancer (NSCLC) with ALK rearrangements. We performed tumor volume analysis of ALK-rearranged advanced NSCLC treated with crizotinib to identify an early predictive marker for prolonged survival.

MATERIALS AND METHODS

Cases of 42 patients with ALK-rearranged advanced NSCLC (16 men, 26 women; median age: 55.7 y) treated with crizotinib as their first ALK-directed therapy were retrospectively studied. Tumor volume measurements of dominant lung lesions were performed on baseline computed tomography and follow-up computed tomography at 8 weeks of therapy. The relationships between the 8-week volume change (%) and overall survival (OS) were investigated.

RESULTS

The 8-week tumor volume change ranged from -99.3% to 117.5% (median: -57.7%). Using the 25th percentile of the 8-week volume change of -74%, 11 patients with >74% volume decrease at 8 weeks had a significantly longer OS compared with 31 patients with ≤74% decrease (median OS: 92.0 vs. 22.8 mo; P=0.0048). In multivariable analyses using Cox proportional hazards models, the 8-week volume decrease of >74% was significantly associated with longer OS (hazard ratio=0.14, 95% confidence interval: 0.03-0.59; Cox P=0.008) after adjusting for tumor stage (stage IV vs. recurrent NSCLC, hazard ratio=5.6, 95% confidence interval: 1.29-24.3; P=0.02).

CONCLUSIONS

The 8-week tumor volume decrease of >74% is significantly associated with longer OS in patients with ALK-rearranged NSCLC treated with crizotinib.

Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment

Simple Summary

In this study we explored the predictive ability of radiomics in non-small cell lung cancer patients, and reported the complementary role of radiomics in predicting the treatment response of the lymph nodes. Radiomics analysis is a cutting-edge technology for the noninvasive assessment of tumor biology, which converts medical images into mineable high-dimensional data. Our method is cost-effective with no need for additional studies, and moreover, we used an easily reproducible study method that can be applicable in further studies using radiomics in oncology.

Abstract

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the pathologic complete response of LNs showing residual uptake after neoadjuvant concurrent chemoradiotherapy (NeoCCRT) in patients with non-small cell lung cancer (NSCLC). Two hundred and thirty-seven patients who underwent NeoCCRT for stage IIIa NSCLC were included. Two hundred fifty-two CT radiomics features were extracted from LNs showing remaining positive FDG uptake upon restaging PET-CT. A multivariable logistic regression analysis of radiomics features and clinicopathologic characteristics was used to develop a prediction model. Of the 237 patients, 135 patients (185 nodes) met our inclusion criteria. Eighty-seven LNs were proven to be malignant (47.0%, 87/185). Upon multivariable analysis, metastatic LNs were significantly prevalent in females and patients with adenocarcinoma (odds ratio (OR) = 2.02, 95% confidence interval (CI) = 0.88–4.62 and OR = 0.39, 95% CI = 0.19–0.77 each). Metastatic LNs also had a larger maximal 3D diameter and higher cluster tendency (OR = 9.92, 95% CI = 3.15–31.17 and OR = 2.36, 95% CI = 1.22–4.55 each). The predictive model for metastasis showed a discrimination performance with an area under the receiver operating characteristic curve of 0.728 (95% CI = 0.654–0.801, p value < 0.001). The radiomics approach allows for the noninvasive detection of metastases in LNs with residual FDG uptake after the treatment of NSCLC patients.

Tumor volume dynamics and tumor growth rate in ALK-rearranged advanced non-small-cell lung cancer treated with crizotinib

Purpose

The purpose of the study is to investigate volumetric tumor burden dynamics and tumor growth rates in ALK-rearranged advanced NSCLC patients during crizotinib monotherapy.

Methods

The study included 44 ALK-rearranged advanced NSCLC patients treated with crizotinib monotherapy as their initial ALK-directed therapy, who had at least one measurable lung lesion and at least two follow-up CT scans, and experienced tumor volume increase while on crizotinib. The tumor volume (in mm³) of the dominant lung lesion was measured on serial CT scans during therapy for analysis of tumor growth rates after the volume nadir.

Results

A total of 231 volume measurements from the nadir to the end of crizotinib therapy or the last follow-up in 44 patients were analyzed in a linear mixed-effects model, fitting time (in months since baseline) as a random effect. When measured from the volume nadir, the tumor growth rate of the logarithm of tumor volume (log_eV) was 0.04/month (SE = 0.012, P = 0.0011) in the unadjusted model. When adjusted for the baseline volume (log_eV₀), the growth rate was again 0.04/month (SE = 0.011, P = 0.0004). When adjusted for clinical variables and log_eV₀, the growth rate was 0.045/month (SE = 0.012, P = 0.0002), indicating that the tumor growth rate after nadir in this cohort remains very close to 0.04/month regardless of log_eV₀ or clinical factors.

Conclusions

Tumor volume growth rate after nadir in ALK-rearranged NSCLC patients treated with crizotinib was obtained, providing objective reference values that can inform physicians when deciding to keep their patients on ALK directed therapy with slowly progressing lung cancer.

Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer

Background

The clinical features and traditional semantic imaging characteristics of BRAF-mutated non-small cell lung cancer (NSCLC) have been previously reported. The radiomic features of BRAF-mutated NSCLC and their role in predicting cancer stage, however, have yet to be investigated. This study’s goal is to assess the differences in CT radiomic features of primary NSCLC driven by BRAF mutation and stratified by tumor-node-metastasis (TNM) staging.

Methods

Our IRB approved study included 62 patients with BRAF mutations (V600 in 27 and non-V600 in 35 patients), who underwent contrast-enhanced chest CT. Tumor stage was determined based on the 8^th edition of TNM staging. Two thoracic radiologists assessed the primary tumor imaging features such, including tumor size (maximum and minimum dimensions) and density (Hounsfield units, HU). De-identified transverse CT images (DICOM) were processed with 3D slicer (Version 4.7) for manual lesion segmentation and estimation of radiomic features. Descriptive statistics, multivariate logistic regression, and receiver operating characteristics (ROC) were performed.

Results

There were significant differences in the radiomic features based on cancer stages I-IV with the most significant differences between stage IV and stage I lesions [AUC 0.94 (95% CI: 0.86–0.99), P<0.04]. There were also significant differences in radiomic features between stage IV and combined stages I-III [40/113 radiomic features; AUC 0.71 (95% CI: 0.59–0.85); P<0.04–0.0001]. None of the clinical (0/6) or imaging (0/3) features were significantly different between stage IV and combined stages I–III.

Conclusions

The radiomic features of primary tumor in BRAF driven NSCLC significantly vary with cancer stage, independent of standard imaging and clinical features.

Radiological assessment after stereotactic body radiation of lung tumours

The increasing use of stereotactic body radiation therapy for lung tumours comes along with new post-therapeutic imaging findings that should be known by physicians involved in patient follow-up. Radiation-induced lung injury is much more frequent than after conventional radiation therapy, it can also be delayed and has a different radiological presentation. Radiation-induced lung injury after stereotactic body radiation therapy involves the lung parenchyma surrounding the target tumour and appears as a dynamic process continuing for years after completion of the treatment. Thus, the radiological pattern and the severity of radiation-induced lung injury are prone to changes during follow-up, which can make it difficult to differentiate from local recurrence. Contrary to radiation-induced lung injury, local recurrence after stereotactic body radiation therapy is rare. Other complications mainly depend on tumour location and include airway complications, rib fractures and organizing pneumonia. The aim of this article is to provide a wide overview of radiological changes occurring after SBRT for lung tumours. Awareness of changes following stereotactic body radiation therapy should help avoiding unnecessary interventions for pseudo tumoral presentations.

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

Decoding the Genomic Report for Radiologists

OBJECTIVE. The purpose of this review is to provide a guide for radiologists that explains the language and format of modern genomic reports and summarizes the relevance of this information for modern oncologic imaging. CONCLUSION. Genomic testing plays a critical role in guiding oncologic therapies in the age of targeted treatments. Understanding and interpreting genomic reports is a valuable skill for radiologists involved with oncologic imaging interpretation.

A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma

To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features. We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model. In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90  ±  0.02 for the training, 0.88  ±  0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837). The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.

18F-FDG PET/CT in Restaging and Evaluation of Response to Therapy in Lung Cancer: State of the Art

BACKGROUND

Metabolic information provided by 18F-FDG PET/CT are useful for initial staging, therapy planning, response evaluation, and to a lesser extent for the follow-up of non-small cell lung cancer (NSCLC). To date, there are no established clinical guidelines in treatment response and early detection of recurrence.

OBJECTIVE

To provide an overview of 18F-FDG PET/CT in NSCLC and in particular, to discuss its utility in treatment response evaluation and restaging of lung cancer.

METHODS

A comprehensive search was used based on PubMed results. From all studies published in English those that explored the role of 18F-FDG PET/CT in the treatment response scenario were selected.

RESULTS

Several studies have demonstrated that modifications in metabolic activity, expressed by changes in SUV both in the primary tumor as well as in regional lymph nodes, are associated with tumor response and survival. Beside SUV, other metabolic parameters (i.e. MTV, TLG, and percentage changes) are emerging to be helpful for predicting clinical outcomes.

CONCLUSION

18F-FDG parameters appear to be promising factors for evaluating treatment response and for detecting recurrences, although larger prospective trials are needed to confirm these evidences and to determine optimal cut-off values.

Advanced urothelial cancer: a radiology update

The recent genomic characterization of urothelial carcinoma by the Cancer Genome Atlas Project, made possible by the introduction of high throughput, reduced cost, and sequence analysis, has shed new insights on the biology of advanced disease. In addition, studies on imaging of advanced urothelial carcinoma have widened the knowledge on disease presentation and on pattern of metastatic spread and their correlation with the underlying biology of urothelial carcinoma. The wide range of treatments for advanced urothelial cancer, including combined chemotherapy regimens and immune checkpoint inhibitors, each result in treatment class-specific patterns of response and adverse events. Results of studies point to the need for a reliable biomarker, perhaps with imaging, that predicts prognosis and treatment response to systemic treatment, and can be used to select the most effective treatment while minimizing toxicity. This review of advanced urothelial cancer introduces the latest advances in genetic profiling, the current role of imaging, the radiographic appearance of treatment response and their toxicities, and details potential future areas of imaging research.

Imaging of Precision Therapy for Lung Cancer: Current State of the Art

Advances in characterization of molecular and genomic abnormalities specific to lung cancer have made precision therapy the current standard of care for lung cancer treatment. This article will provide a cutting-edge review of imaging of lung cancer in the current era of precision medicine. The focus of the article includes (a) an update on the recent advances in precision therapy for non-small cell lung cancer and their implications on imaging; (b) molecular and genomic biomarkers and pitfalls of image interpretations for lung cancer precision therapy; and (c) review of the current approaches and future directions of precision imaging for lung cancer, emphasizing emerging observations in longitudinal tumor kinetics, radiomics, and molecular and functional imaging. The article is designed to help radiologists to remain up to date in the rapidly evolving world of lung cancer therapy and serve as key members of multidisciplinary teams caring for these patients.

A Radiologist's Guide to the Changing Treatment Paradigm of Advanced Non-Small Cell Lung Cancer: The ASCO 2018 Molecular Testing Guidelines and Targeted Therapies

OBJECTIVE. The purpose of this article is to provide an imaging-based guide of the modern genomic classifications and targeted therapies for advanced non-small cell lung cancer (NSCLC) with an emphasis on the relevance of the 2018 American Society of Clinical Oncology molecular testing guidelines for radiologists. CONCLUSION. Knowledge of the radiologic relevance of lung cancer driver mutations and modern targeted agents is essential for imaging interpretation of advanced NSCLC in the modern age of precision medicine.

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.

Evaluation of response to conventional chemotherapy and radiotherapy by perfusion computed tomography in non-small cell lung cancer (NSCLC)

BACKGROUND

To evaluate changes in perfusion computed tomography (PCT) parameters induced by treatment with conventional chemotherapy (CCT) alone or with CCT and radiation therapy (RT) in patients with non-small cell lung cancer (NSCLC) and to determine whether these changes correlate with response as defined by the response evaluation criteria in solid tumours version 1.1 (RECIST-1.1).

METHODS

Fifty-three patients with a histological diagnosis of NSCLC prospectively underwent PCT of the whole tumour, before/after CCT or before/after CCT and RT. Blood flow (BF), blood volume (BV), permeability (PMB), and mean transit time (MTT) were compared before and after treatment and with the response as defined by RECIST-1.1. The relationship between changes in the perfusion parameters and in tumour size was also evaluated.

RESULTS

PCT parameters decreased after treatment, significantly for BV (p = 0.002) and MTT (p = 0.027). The 30 patients with partial response had a significant decrease of 21% for BV (p = 0.006) and 17% for MTT (p = 0.031). A non-significant decrease in all perfusion parameters was found in patients with stable disease (p > 0.137). In patients with progressive disease, MTT decreased by 10% (p = 0.465) and the other parameters did not significantly vary (p > 0.809). No significant correlation was found between changes in size and PCT parameters (p > 0.145).

CONCLUSIONS

Treatment of NSCLC with platinum derivatives, with or without RT, induces changes in PCT parameters. Partial response is associated with a significant decrease in BV and MTT, attributable to the effect of the treatment on tumour vascularisation.

Interstitial lung abnormalities: prognostic stratification of subtle radiological findings

PURPOSE OF REVIEW

The purpose of this review is to provide the radiological description of interstitial lung abnormalities (ILA) as an increasingly reported entity on high-resolution computed tomography (HRCT), and to discuss their prospective interpretation.

RECENT FINDINGS

Elementary findings consistent with ILA are described on HRCT, yet the diagnostic confidence for the interpretation of these subtle features might be challenging and further hampered by interobserver variability. Quantitative analysis is expected to provide standardized and reproducible description of ILA. There is affinity between ILA morphology and histopathological pattern, either fibrosis or atypical adenomatous hyperplasia. Beyond radiology, there are predictors of risk of ILA, such as: age, smoking habit, circulating biomarkers, and genetic sequencing. ILA with fibrotic morphology show prognostic impact including progression to interstitial lung disease, mortality from respiratory disease, and all-cause mortality. The association between ILA and susceptibility to lung damage further includes the interlacing connection between interstitial findings and lung cancer, both as a risk factor for diagnosis and as a predictor of survival.

SUMMARY

ILA are a (minor) finding on HRCT and they should be reported by radiologists for optimal management within the specific clinic-functional scenario. ILA encompass a number of semiological characteristics associated with either fibrotic or nonfibrotic disease.

The incidence of ALK inhibitor-related pneumonitis in advanced non-small-cell lung cancer patients: A systematic review and meta-analysis

INTRODUCTION

We evaluated the incidence of pneumonitis in clinical trials of anaplastic lymphoma kinase (ALK) inhibitors in patients with advanced non-small cell lung cancer (NSCLC) and compared the incidence among different cohorts, in order to identify possible predisposing factors for ALK inhibitor-related pneumonitis.

METHODS

MEDLINE and EMBASE search up to 1/30/18 using the keywords, "alectinib", "ceritinib", "crizotinib", "brigatinib", and "lung cancer", resulting in a total of 20 eligible cohorts with 2261 patients treated with ALK inhibitor monotherapy for advanced NSCLC. The pooled incidences of all-grade, high-grade, and grade 5 pneumonitis were calculated. Subgroup analyses were conducted with meta-regression using study-level covariates.

RESULTS

The overall pooled incidence of pneumonitis was 2.14% (95% CI: 1.37-3.34) for all grade, 1.33% (95% CI: 0.80-2.21) for high grade, and 0.22% (95% CI: 0.09-0.52) for grade 5 pneumonitis. The incidence was significantly higher in studies from Japan compared to studies of non-Japan origin, for all-grade (6.25% vs 1.14%, p < 0.001) and high-grade pneumonitis (3.31% vs 0.39%, p < 0.001). Multivariate meta-regression demonstrated the cohorts from Japanese studies had significantly higher odds of pneumonitis for all-grade (odds ratio [OR]: 4.329 [95% CI: 1.918, 9.770], p < 0.001) compared to those of non-Japan origin, after adjusting for types of ALK inhibitors.

CONCLUSIONS

The overall incidence of ALK inhibitor pneumonitis was 2.14% in patients with advanced NSCLS. The patients from Japanese cohorts had a higher incidence of ALK-inhibitor pneumonitis, which indicates the need for increased awareness and caution for pneumonitis in Japanese patients treated with ALK inhibitors.

Are we ready to describe response or progression to immunotherapy in lung cancer?

Immune checkpoint inhibitors have changed history and management of different tumor types, including non-small cell lung cancer (NSCLC). Response patterns may be more heterogeneous than those seen with cytotoxic chemotherapy. Besides atypical response patterns, new types of outcome should be taken into account such as pseudo-progression (PP) and hyper-progressive disease (HPD). PP is described as initial tumor increase or appearance of new lesions followed by their shrinkage during immunotherapy treatment while HPD is a rapid and severe pattern of progression with a not yet univocal definition. Physiopathology and underlying mechanism of these phenomena are not completely understood and in absence of reliable clinical and biological markers of response to immunotherapy, radiological evaluation remains a key point in clinicians' decision-making process but further efforts would be useful to identify a unique system of evaluation.In this review we summarize the main radiological criteria available in the evaluation of response to checkpoint inhibitors and we describe peculiar response patterns such PP and HPD with a focus on lung cancer.

Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy

PURPOSE

To directly compare the utility for therapeutic outcome prediction of dynamic first-pass contrast-enhanced (CE)-perfusion area-detector computed tomography (ADCT), MR imaging assessed with the same mathematical method and 2-[fluorine-18]-fluoro-2-deoxy-d-glucose-positron emission tomography combined with CT (PET/CT) for non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy.

MATERIALS AND METHODS

Forty-three consecutive stage IIIB NSCLC patients, consisting of 25 males (mean age ± standard deviation: 66.6 ± 8.7 years) and 18 females (66.4 ± 8.2 years) underwent PET/CT, dynamic CE-perfusion ADCT and MR imaging, chemoradiotherapy, and follow-up examination. In each patient, total, pulmonary arterial, and systemic arterial perfusions were calculated from both perfusion data and SUV_max on PET/CT, assessed for each targeted lesion, and averaged to determine final values. Receiver operating characteristics analyses were performed to compare the utility for distinguishing responders from non-responders using Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria. Overall survival (OS) assessed with each index were compared between two groups by means of the Kaplan-Meier method followed by the log-rank test.

RESULTS

Area under the curve (Az) for total perfusion on ADCT was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Az of total perfusion on MR imaging was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Mean OS of responder and non-responder groups were significantly different for total and systemic arterial (P < 0.05) perfusion.

CONCLUSION

Dynamic first-pass CE-perfusion ADCT and MR imaging as well as PET/CT are useful for early prediction of treatment response by NSCLC patients treated with chemoradiotherapy.

Imaging of Histiocytosis in the Era of Genomic Medicine

Histiocytosis describes a group of diseases that have long been considered enigmatic in the history of medicine. Recently, novel genomic analyses have identified somatic oncogenic driver mutations responsible for the pathogenesis of these entities. These discoveries have led to the recharacterization of histiocytoses as neoplastic diseases and have opened a new era of precision medicine approaches for treatment. The histiocytic disorders demonstrate a variety of imaging manifestations involving multiple organ systems, and radiologists play a major role in diagnosis and monitoring. An up-to-date knowledge of the novel genomic discoveries and their implications is essential for radiologists to understand the new approaches to treating histiocytic disorders and to contribute as key members of the multidisciplinary treatment team. This article provides a cutting-edge review of the novel concepts in histiocytosis, with a focus on recent genomic discoveries and precision medicine approaches to treating the disease, and describes imaging manifestations with correlative histologic and genomic findings, with an emphasis on adult-onset cases and uncommon subtypes. ^©RSNA, 2018.

Tumor Response Assessment for Precision Cancer Therapy: Response Evaluation Criteria in Solid Tumors and Beyond

Objective assessment of tumor responses and treatment results has been the basis for the advancement of cancer therapies, and imaging plays a key role to provide a "common language" to describe the results of cancer treatment. Although Response Evaluation Criteria in Solid Tumors (RECIST) has been the most widely accepted method for assessing tumor response in the past decades, the limitations of RECIST have increasingly becoming recognized, especially with the recent advances of precision-medicine approaches to cancer. This article reviews the current concept of tumor response evaluations based on RECIST, describes the limitations of RECIST, and proposes strategies to overcome the limitations. The article emphasizes specific limitations in the setting of precision cancer therapy and cancer immunotherapy and discusses the important insights provided by the cutting-edge investigations in the emerging fields.

Automated image analysis tool for tumor volume growth rate to guide precision cancer therapy: EGFR-mutant non-small-cell lung cancer as a paradigm

PURPOSE

To develop an automated analytic module for calculation of tumor growth rate from serial CT scans and to apply the module and evaluate reproducibility in a pilot cohort of advanced NSCLC patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors.

MATERIALS AND METHODS

The module utilized a commercially available image-processing workstation equipped with a validated tumor volume measurement tool. An automated analytic software module was programmed with the capability to record and display serial tumor volume changes and to calculate tumor volume growth rate over time and added to the workstation. The module was applied to evaluate the tumor growth rate in a pilot cohort of 24 EGFR-mutant patients treated with EGFR inhibitors, and reproducibility references as tested by two independent thoracic radiologists.

RESULTS

The module analyzed chest CT scans from 24 patients (5 males, 19 females; median age: 61) with a median of 8 scans per patient, totaling 227 scans and provided a graphical display with an automated and instant calculation of tumor growth rate after the nadir volume for each patient. High inter and intraobserver agreements were noted for tumor growth rates, with concordance correlation coefficients of 0.9323 and 0.9668, respectively. Interpretation of slow versus fast tumor growth using previously identified threshold of ≤0.15/month had a perfect interobserver agreement (κ = 1.00), and an excellent intraobserver agreement (κ = 0.895).

CONCLUSIONS

The present study describes the development of an image analytic module for assessing tumor growth rate and the data demonstrates the functionality and reproducibility of the module in a pilot cohort of EGFR-mutant NSCLC patients treated with EGFR-TKI. The image analytic module is an initial step for clinical translation of the tumor growth rate approach to guide cancer treatment in precision oncology.

Non-Small Cell Lung Cancer with Resistance to EGFR-TKI Therapy: CT Characteristics of T790M Mutation-positive Cancer

Purpose To evaluate the clinical and CT characteristics of T790M mutation-positive non-small cell lung cancer (NSCLC) after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy failure. Materials and Methods A retrospective study of 304 patients with NSCLC who underwent rebiopsy after first-line EGFR-TKI therapy was conducted. Rebiopsy methods included CT- or fluoroscopy-guided lung biopsies (n = 105), endobronchial US- or bronchofibroscopy-guided biopsies (n = 66), pleural fluid analysis (n = 47), other solid organ biopsies (n = 43), US-guided axillary or supraclavicular lymph node biopsies (n = 31), and cerebrospinal fluid analysis (n = 12). CT findings at the initial diagnosis and rebiopsy were analyzed. Progression-free survival, the duration from the start of TKI therapy to rebiopsy, and survival were calculated. Results At rebiopsy, 144 (47.4%) patients were T790M mutation positive. The percentages of T790M mutation-positive NSCLCs were similar in 106 patients with rebiopsy of the lungs (53 [50%] of 106) and in 77 patients with rebiopsy of the primary lung lesions (36 [47%] of 77). T790M mutation positivity was associated with peripheral tumors (odds ratio [OR], 2.6; P = .01), pleural tag (OR, 5.0; P < .001), and air bronchogram (OR, 4.0; P = .006) at CT after TKI failure. The duration from the start of TKI therapy to rebiopsy was longer in T790M mutation-positive than in T790M mutation-negative patients (20.5 vs 13.6 months; P < .001). Cumulative survival from the time of rebiopsy to the last follow-up was significantly longer in patients with T790M mutation-positive lung cancers (P = .014). However, median survival time after rebiopsy was not statistically different between patients with and those without T790M mutation. Conclusion Peripheral tumor location with vascular convergence, the presence of a pleural tag, and air bronchogram of lung lesions at CT at the time of rebiopsy were significantly associated with T790M mutation in patients with non-small cell lung cancer after first-line epidermal growth factor receptor-tyrosine kinase inhibitor therapy failure. © RSNA, 2018 Online supplemental material is available for this article.

Hallmarks of Cancer in the Reading Room: A Guide for Radiologists

OBJECTIVE

The hallmarks of cancer are mechanisms that cells develop to undergo malignant transformation. The targeting of these hallmarks by newer cancer therapies results in new mechanisms of response, toxicity, and resistance. The purpose of this article is to review these hallmarks, their associated targeted therapies, imaging features of responses, and toxicities.

CONCLUSION

Ten hallmarks, among them proliferative signaling, angiogenesis, immune response, and genome instability, are reviewed. Molecular targeted therapies, including antiangiogenic factors and immune checkpoint inhibitors, target these hallmarks.

Imaging on Lung Cancer and Treatment with Targeted Therapy

The identification of genetic mutations known as oncogenic driver mutations that lead to the growth and survival of cancer cells has been an important advance in the field of oncology. Treatment in advanced non-small-cell lung cancer (NSCLC) has transitioned from a more general approach to a more personalized approach based on genetic mutations of the cancer itself. Common mutations detected in patients with advanced NSCLC include mutations of epidermal growth factor receptor and anaplastic lymphoma kinase (ALK). Targeted therapies are aimed at the products of these gene mutations and include erlotinib (used in epidermal growth factor receptor mutant NSCLC) and crizotinib (used in anaplastic lymphoma kinase positive NSCLC). In this review, we discuss common genetic mutations in advanced NSCLC, the role of targeted therapies, and imaging findings that can be associated with various genetic mutations.

Targeted Therapy and Imaging Findings

Non-small cell lung cancer (NSCLC) is usually diagnosed when it is not amenable to curative surgery or radiation. Many of these patients are candidates for systemic therapy. Median survival is only approximately 10 months, and, accordingly, treatment in advanced NSCLC is evolving toward a more personalized approach with the identification of genetic abnormalities based on biomarkers. For example, gene mutations in EGFR (epidermal growth factor receptor) and ALK (anaplastic lymphoma kinase) lead to a cascade of pathways resulting in uncontrolled growth, proliferation, and survival of tumor cells. Targeted therapies are aimed at the products of these mutated genes and include agents such as erlotinib and gefitinib (in EGFR-mutant NSCLC) or crizotinib (in ALK-positive NSCLC). Antiangiogenesis agents such as bevacizumab are another category of targeted therapy that inhibits vascular endothelial growth factors. The imaging characteristics of advanced NSCLC with genetic abnormalities, the evolution of targeted therapies and their imaging manifestations will be discussed.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

Targeted Therapy and Immunotherapy in the Treatment of Non-Small Cell Lung Cancer

The treatment strategy in advanced non-small cell lung cancer (NSCLC) has evolved from empirical chemotherapy to a personalized approach based on histology and molecular markers of primary tumors. Targeted therapies are directed at the products of oncogenic driver mutations. Immunotherapy facilitates the recognition of cancer as foreign by the host immune system, stimulates the immune system, and alleviates the inhibition that allows the growth and spread of cancer cells. The authors describes the role of targeted therapy and immunotherapy in the treatment of NSCLC, patterns of disease present on imaging studies, and immune-related adverse events encountered with immunotherapy.