Measures of response: RECIST, WHO, and new alternatives

Radiology and multi-scale data integration for precision oncology

In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.

Spectrum of Imaging Findings and Complications After Hepatic Transarterial Chemoembolization for Liver Tumors

ABSTRACT

This study reviews the spectrum of imaging findings and complications after transarterial chemoembolization (TACE) for the treatment of primary liver tumors (hepatocellular carcinoma, cholangiocarcinoma) and liver metastases. The review encompasses a spectrum of imaging criteria for assessing treatment response, including the modified Response Evaluation Criteria in Solid Tumors guidelines, tumor enhancement, and apparent diffusion coefficient alterations.We discuss the expected posttreatment changes and imaging responses to TACE, describing favorable and poor responses. Moreover, we present cases that demonstrate potential complications post-TACE, including biloma formation, acute cholecystitis, abscesses, duodenal perforation, arterial injury, and nontarget embolization. Each complication is described in detail, considering its causes, risk factors, clinical presentation, and imaging characteristics.To illustrate these findings, a series of clinical cases is presented, featuring diverse imaging modalities including computed tomography, magnetic resonance imaging, and digital subtraction angiography.

A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns

Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.

Progression-free survival, disease-free survival and other composite end points in oncology: improved reporting is needed

Composite outcome measures such as progression-free survival and disease-free survival are increasingly used as surrogate end points in oncology research, frequently serving as the primary end point of pivotal trials that form the basis for FDA and EMA approvals. Such outcome measures combine two or more distinct events (for example, tumour (re)growth, new lesions and/or death) into a single, time-to-event end point. The use of a composite end point can increase the statistical power of a clinical trial and decrease the follow-up period required to demonstrate efficacy, thus lowering costs; however, these end points have a number of limitations. Composite outcomes are often vaguely defined, with definitions that vary greatly between studies, complicating comparisons of results across trials. Altering the makeup of events included in a composite outcome can alter study conclusions, including whether treatment effects are statistically significant. Moreover, the events included in a composite outcome often vary in clinical significance, reflect distinct biological pathways and/or are affected differently by treatment. Therefore, knowing the precise breakdown of the component events is essential to accurately interpret trial results and gauge the true benefit of an intervention. In oncology clinical trials, however, such information is rarely provided. In this Perspective, we emphasize this deficiency through a review of 50 studies with progression-free survival as an outcome published in five top oncology journals, discuss the advantages and challenges of using composite end points, and highlight the need for transparent reporting of the component events.

Desmoid Tumors in the Active Surveillance Era: Evaluation of Treatment Options and Pain Relief in a Single-Center Retrospective Analysis

In patients with desmoid tumors (DTs), active surveillance has been increasingly preferred over surgery, while treatment (including pharmacological therapy, radiotherapy, and/or surgery) is performed in cases with confirmed disease progression. This study aimed to evaluate event-free survival and pain management according to different treatment strategies. We evaluated event-free survival, including recurrence after initial surgical treatment or changes in the therapeutic management after initial non-surgical treatment and pain management according to different treatment strategies. All patients referred for DT in 2001-2021 at our institutions were stratified into four groups: those treated surgically prior to 2012 (SGPre12) or after 2012 (SGPost12), those treated pharmacologically (MG), and those under active surveillance (ASG). An event was defined as recurrence after initial surgical treatment or a change in therapeutic management. Overall, 123 patients were included in the study: 28 in SGPre12, 41 in SGPost12, 38 in MG, and 16 in ASG. Pharmacological treatment resolved painful symptoms in 16/27 (60%) patients (p = 0.0001). The median follow-up duration was 40 months (IQR 23-74). Event-free survival at 1, 3, and 5 years was: 85%, 70%, and 62% in SGPre12; 76%, 58%, and 49% in SGPost12; 49%, 31%, and 31% in MG; and 45%, 45%, and 45% in ASG. Our findings support the role of active surveillance as initial management, as demonstrated by the fact that about half the patients did not experience any progression, while surgery can be reserved as a first-line approach for selected patients. In terms of pain relief, medical therapy led to symptom resolution in more than half the cases.

Universal Lesion Detection Utilising Cascading R-CNNs and a Novel Video Pretraining Method

According to the WHO, approximately one in six individuals worldwide will develop some form of cancer in their lifetime. Therefore, accurate and early detection of lesions is crucial for improving the probability of successful treatment, reducing the need for more invasive treatments, and leading to higher rates of survival. In this work, we propose a novel R-CNN approach with pretraining and data augmentation for universal lesion detection. In particular, we incorporate an asymmetric 3D context fusion (A3D) for feature extraction from 2D CT images with Hybrid Task Cascade. By doing so, we supply the network with further spatial context, refining the mask prediction over several stages and making it easier to distinguish hard foregrounds from cluttered backgrounds. Moreover, we introduce a new video pretraining method for medical imaging by using consecutive frames from the YouTube VOS video segmentation dataset which improves our model's sensitivity by 0.8 percentage points at a false positive rate of one false positive per image. Finally, we apply data augmentation techniques and analyse their impact on the overall performance of our models at various false positive rates. Using our introduced approach, it is possible to increase the A3D baseline's sensitivity by 1.04 percentage points in mFROC.

Colonoscopy, imaging, and carcinoembryonic antigen: Comparison of guideline adherence to surveillance strategies in patients who underwent resection of colorectal cancer - A systematic review and meta-analysis

INTRODUCTION

Almost one-third of patients with colorectal cancer (CRC) experience recurrence after resection. Adherence to surveillance guidelines largely dictates efficacy in early detection of recurrence. We sought to assess and compare adherence to postoperative surveillance guidelines for colonoscopy, imaging, and Carcinoembryonic Antigen (CEA).

METHODS

PubMed, Medline, Embase, Scopus, Cochrane, Web of Science, and CINAHL were systematically searched. Random-effects meta-analysis was performed and pooled adherence to each surveillance strategy was assessed for CEA, imaging, and colonoscopy.

RESULTS

Overall 14 studies (55,895 patients) met the inclusion criteria. Adherence to colonoscopy guidelines was the highest (70%, 95%CI 67-73), followed by imaging (63%, 95%CI 47-80), and CEA (54%; 95%CI 42-66). Among 7 (50%) studies that examined adherence to the American Society of Clinical Oncology guidelines, compliance with colonoscopy was the highest (73%; 95% CI 70-76), followed by imaging (58%; 95% CI 37-78), and CEA (45%; 95%CI 37-52). Of note, guideline adherence to CEA testing was much lower than colonoscopy among patients with colon (OR 0.21; 95%CI 0.20-0.22) and rectal cancer (OR 0.25; 95%CI 0.23-0.28) (both p < 0.05). This was also noted when compared with imaging recommendations among older patients (OR = 0.62; 95%CI 0.42-0.93) and patients with stage II, (OR = 0.80; 95%CI 0.76-0.84) and stage III disease (OR = 0.88; 95%CI 0.82-0.94) (all p < 0.05).

CONCLUSION

While guideline adherence to postoperative surveillance with colonoscopy was high, adherence to CEA testing and imaging surveillance strategies was markedly lower following CRC resection. Future studies should investigate avenues to improve compliance with surveillance guidelines among health care providers and patients to optimize postoperative follow-up for CRC.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation

BACKGROUND

Although curative surgery remains the cornerstone of the therapeutic strategy in patients with soft tissue sarcomas (STS), neoadjuvant radiotherapy and chemotherapy (NART and NACT, respectively) are increasingly used to improve operability, surgical margins and patient outcome. The best imaging modality for locoregional assessment of STS is MRI but these tumors are mostly evaluated in a qualitative manner.

OBJECTIVE

After an overview of the current standard of care regarding treatment for patients with locally advanced STS, this review aims to summarize the principles and limitations of (i) the current methods used to evaluate response to neoadjuvant treatment in clinical practice and clinical trials in STS (RECIST 1.1 and modified Choi criteria), (ii) quantitative MRI sequences (i.e., diffusion weighted imaging and dynamic contrast enhanced MRI), and (iii) texture analyses and (delta-) radiomics.

Current Advances and Challenges in Radiomics of Brain Tumors

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

Undervaluation of Radiotherapy for Gross Desmoid Tumors: The Need for Absolute Volume Assessment

BACKGROUND/AIM

To compare absolute volume (AV) assessment according to Response Evaluation Criteria in Solid Tumors (RECIST) for the response evaluation of desmoid tumors (DTs) treated with radiotherapy.

PATIENTS AND METHODS

Eighteen patients with DTs ≥3 cm in size were included.

RESULTS

The median follow-up duration was 78.0 months. Five patients achieved a complete response according to RECIST, seven reached a partial response (PR), and one eventually exhibited progression. The overall response rate was 61%, the median time to PR was 8.0 months. Six patients achieved stable disease, although three developed progressions. Of the six patients with a PR, the median change in maximum diameter was -46%, and the median change in maximum volume was -84%. Three patients could have been diagnosed with progression at least 6 months earlier if the AV increment was considered.

CONCLUSION

An AV assessment is essential for an accurate response assessment of DTs and radiotherapy seems feasible as a first-line treatment for DTs.

Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma

BACKGROUND

The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging.

OBJECTIVE

To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC.

METHODS

A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance.

RESULTS

The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification.

CONCLUSIONS

The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.

Comparison of CT Volumetry and RECIST to Predict the Treatment Response and Overall Survival in Gastric Cancer Liver Metastases

Purpose

The aim of this study was to compare the diameter and volume of liver metastases on CT images in relation to overall survival and tumor response in patients with gastric cancer liver metastases (GCLM) treated with chemotherapy.

Materials and Methods

We recruited 43 patients with GCLM who underwent chemotherapy as a first-line treatment. We performed a three-dimensional quantification of the metastases for each patient. An independent survival analysis using the Response Evaluation Criteria in Solid Tumors (RECIST) was performed and compared to volumetric measurements. Overall survival was evaluated using Kaplan-Meier analysis and compared using Cox proportional hazard ratios following univariate analyses.

Results

When patients were classified as responders or non-responders based on volumetric criteria, the median overall survival was 23.6 months [95% confidence interval (CI), 8.63–38.57] and 7.6 months (95% CI, 3.78–11.42), respectively (p = 0.039). The volumetric analysis and RECIST of the non-progressing and progressing groups showed similar results based on the Kaplan-Meier method (p = 0.006) and the Cox proportional hazard model (p = 0.008).

Conclusion

Volumetric assessment of liver metastases could be an alternative predictor of overall survival for patients with GCLM treated with chemotherapy.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume

Purpose

Materials and Methods

Results

Conclusion

Correlation between initial tumour volume and treatment duration on Dabrafenib: observation study of subjects with BRAF mutant melanoma on the BRF112680 trial

BACKGROUND

Planar-based measurements of lesions in metastatic melanoma have limitations in estimating tumor burden of a patient and in predicting response to treatment. Volumetric imaging might add predictive value to Response criteria in Solid Tumor (RECIST)-measurement. Based on clinical observations, we explored the association between baseline tumor volume (TV) and duration of treatment with dabrafenib in patients with metastatic melanoma. We have also explored the prognostic value of TV for overall survival (OS) and progression free survival (PFS).

METHODS

This is a retrospective, chart-review of primary source documents and medical imaging of a cohort of patients participating in the BRF112680 phase 1 clinical trial at the Prince of Wales Hospital. TV was quantified by contouring all the measurable baseline target lesions in the standard manner for radiation planning using Voxxar™ software. We used Cox regression models to analyse associations between TV and duration of treatment with dabrafenib and between TV, PFS and OS.

RESULTS

Among 13 patients of BRAF 112680 trial, 10 were included in the retrospective analysis. Target lesion sum volume ranged from 0.3 to 1065.5 cm³ (cc), with a median of 27.5 cc. The median PFS and OS were 420 days (range 109-1765) and 1680 days (range 390-2940), respectively. The initial TV was inversely correlated with duration of treatment with dabrafenib (rho - 0.6; P 0.03). In multivariate analysis, TV was a predictor for OS (HR 2.81 CI 1.06-6.19) and PFS (8.76 (CI 1.05-43.58). Patients with tumour volume above the median had significantly lower OS of 6-months compared to 56-months survival for patients with smaller volumes; P = 0.019.

CONCLUSIONS

TV is a predictor for treatment duration and is prognostic of OS and PFS in patients with metastatic melanoma. These findings need to be validated prospectively in clinical trials.

The role of neutrophil-to-lymphocyte ratio in predicting overall survival in patients undergoing laser interstitial thermal therapy for glioblastoma

Laser interstitial thermal therapy (LITT) offers a minimally-invasive treatment option for glioblastomas (GBM) which are relatively small or in eloquent areas. While laser ablation for malignant gliomas has been shown to be safe and effective, the role of the subsequent immune response in not well established. In this study we aim to analyze the prognostic potential of edema volume and acute inflammation, quantified as neutrophil-to-lymphocyte ratio (NLR), in predicting overall survival. Twenty-one patients were identified with new or recurrent GBMs that were candidates for LITT. Laser ablation was performed using standard solid tumor protocol for treatment volume, intensity and duration. Edema volume was quantified using MRI imaging, while retrospective chart review was performed to calculate NLR and survival. In patients treated with LITT for GBM, peri-tumoral vasogenic edema volumes did not significantly change post-operatively, p > 0.200, while NLR significantly increased, p = 0.0002. The degree of NLR increase correlated with longer overall survivals, and ROC analysis demonstrated an area under the curve of 0.827, p = 0.0112. A delta-NLR cutoff of 7.0 results in positive and negative predictive values of 78% and 75%, respectively, in predicting overall survival >1 year. Patients with with delta-NLR > 7.0 lived significantly longer that those with delta-NLR < 7.0, median survival 440 days compared to 239 days, p = 0.0297. We demonstrate preliminary data that monitoring the inflammatory response after LITT in GBM patients offers a potential prognostic measurement to assist in predicting treatment efficacy and overall survival.

The stability of imaging biomarkers in radiomics: a framework for evaluation

This paper studies the sensitivity of a range of image texture parameters used in radiomics to: (i) the number of intensity levels, (ii) the method of quantisation to select the intensity levels and (iii) the use of an intensity threshold. 43 commonly used texture features were studied for the gross target volume outlined on the CT component of PET/CT scans of 50 patients with non-small cell lung carcinoma (NSCLC). All cases were quantised for all values between 4 and 128 intensity levels using four commonly used quantisation methods. All results were analysed with and without a threshold range of  -200 HU to 300 HU. Cases were ranked for each texture feature and for all quantisation methods with the Spearman's rank correlation coefficient determined to evaluate stability. Results showed large fluctuations in ranking, particularly for low numbers of levels, differences between quantisation methods and with the use of a threshold, with values Spearman's Rank Correlation for many parameters below 0.2. Our results demonstrated the sensitivity of radiomics features to the parameters used during analysis and highlight the risk of low reproducibility comparing studies with slightly different parameters. In terms of the lung cancer CT datasets, this study supports the use of 128 intensity levels, the same uniform quantiser applied to all scans and thresholding of the data. It also supports several of the features recommended in the literature for such studies such as skewness and kurtosis. A recommended framework is presented for curation of the data analysis process to ensure stability of results.

Agreement Between the European Organization for Research and Treatment of Cancer and Positron Emission Tomography Response Criteria in Solid Tumors in Evaluating Treatment Response in Solid Malignant Tumors

Introduction Fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) is used for non-invasive staging and restaging of solid malignant tumors. PET-CT based criteria have been developed to evaluate the response to targeted therapy. These include the European Organization for Research and Treatment of Cancer (EORTC) and the PET Response Criteria in Solid Tumors (PERCIST). The aim of this study was to determine the agreement between EORTC and PERCIST criteria for treatment response evaluation in patients with solid malignant tumors. Materials and methods This was a retrospective study conducted from February 2017 till July 2017. Electronic medical records of patients diagnosed with solid malignant tumors were searched. Experienced radiologists evaluated the PET-CT images based on EORTC and PERCIST criteria. The Kappa (κ) test was used for evaluation of agreement between treatment response according to EORTC and PERCIST criteria. Results Out of 54 patients, 41 (75.9%) were male and 13 (24.1%) were female with a mean age of 57.09 ± 10.65 years. According to EORTC criteria, complete metabolic response (CMR) was seen in five (9.3%) of patients, partial metabolic response (PMR) was seen in 36 (66.7%) of patients, progressive metabolic disease (PMD) was seen in nine (16.7%) of patients and stable metabolic disease (SMD) was seen in four (7.4%) of patients. According to PERCIST criteria, CMR was seen in five (9.3%) of patients, PMR was seen in 33 (61.1%) of patients, PMD was seen in nine (16.7%) of patients and SMD was seen in seven (13.0%) of patients. EORTC and PERCIST agreed on 43 (79.6%) of the patients with κ-coefficient of 0.62 indicating good agreement (p-value of <0.001). Conclusion EORTC and PERCIST criteria have a good agreement in evaluating treatment response in solid malignant tumors. Therefore, adoption of EORTC or PERCIST in PET-CT reporting can standardize the evaluation of oncological treatment results.

Heterogeneous radiological response to neoadjuvant therapy is associated with poor prognosis after resection of colorectal liver metastases

INTRODUCTION

Surgery combined with perioperative chemotherapy has become standard of care in patients with resectable colorectal liver metastases. However, poor outcome is expected for a significant subgroup. The clinical implications of inter-metastatic heterogeneity remain largely unknown. In a prospective, population-based series of patients undergoing resection of multiple colorectal liver metastases, the aim was to investigate the prevalence and prognostic impact of heterogeneous response to neoadjuvant chemotherapy.

MATERIALS AND METHODS

Radiological response to treatment was evaluated in a lesion-specific manner in 2-5 metastases per patient. Change of lesion diameter was evaluated and response/progression was classified according to three different size thresholds; 3, 4 and 5 mm. A heterogeneous response was defined as progression and response of different metastases in the same patient.

RESULTS

In total, 142 patients with 585 liver metastases were examined with the same radiological method (MRI or CT) before and after neoadjuvant treatment. Heterogeneous response to treatment was seen in 16 patients (11%) using the 3 mm size change threshold, and this group had a 5-year cancer-specific survival of 19% compared to 49% for patients with response in all lesions (p = 0.003). Cut-off values of 4-5 mm were less sensitive for detecting a heterogeneous response, but the survival difference was similar and significant.

CONCLUSION

A subgroup of patients with multiple colorectal liver metastases had heterogeneous radiological response to neoadjuvant chemotherapy and poor prognosis. The evaluation of response pattern is easy to perform, feasible in clinical practice and, if validated, a promising biomarker for treatment decisions.

Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology

Unlabelled Image.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge

RATIONALE AND OBJECTIVES

To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge.

MATERIALS AND METHODS

The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2). The volumes of the real and simulated lesions were compared based on bias (measured mean volume differences between physical and virtually inserted lesions in phantoms as quantified by segmentation algorithms), repeatability, reproducibility, equivalence (phantom phase), and overall QIBA compliance (phantom and clinical phase).

RESULTS

For phantom phase, three of eight groups were fully QIBA compliant, and one was marginally compliant. For compliant groups, the estimated biases were -1.8 ± 1.4%, -2.5 ± 1.1%, -3 ± 1%, -1.8 ± 1.5% (±95% confidence interval). No virtual insertion method showed statistical equivalence to physical insertion in bias equivalence testing using Schuirmann's two one-sided test (±5% equivalence margin). Differences in repeatability and reproducibility across physical and simulated lesions were largely comparable (0.1%-16% and 7%-18% differences, respectively). For clinical phase, 7 of 16 groups were QIBA compliant.

CONCLUSION

Hybrid datasets yielded conclusions similar to real computed tomography datasets where phantom QIBA compliant was also compliant for hybrid datasets. Some groups deemed compliant for simulated methods, not for physical lesion measurements. The magnitude of this difference was small (<5.4%). While technical performance is not equivalent, they correlate, such that, volumetrically simulated lesions could potentially serve as practical proxies.

An integrated stress response via PKR suppresses HER2+ cancers and improves trastuzumab therapy

Trastuzumab is integral to HER2+ cancer treatment, but its therapeutic index is narrowed by the development of resistance. Phosphorylation of the translation initiation factor eIF2α (eIF2α-P) is the nodal point of the integrated stress response, which promotes survival or death in a context-dependent manner. Here, we show an anti-tumor function of the protein kinase PKR and its substrate eIF2α in a mouse HER2+ breast cancer model. The anti-tumor function depends on the transcription factor ATF4, which upregulates the CDK inhibitor P21^CIP1 and activates JNK1/2. The PKR/eIF2α-P arm is induced by Trastuzumab in sensitive but not resistant HER2+ breast tumors. Also, eIF2α-P stimulation by the phosphatase inhibitor SAL003 substantially increases Trastuzumab potency in resistant HER2+ breast and gastric tumors. Increased eIF2α-P prognosticates a better response of HER2+ metastatic breast cancer patients to Trastuzumab therapy. Hence, the PKR/eIF2α-P arm antagonizes HER2 tumorigenesis whereas its pharmacological stimulation improves the efficacy of Trastuzumab therapy.

The HER2 monoclonal antibody, Trastuzumab, is the current standard treatment for HER2+ cancers but resistance to therapy occurs. Here, the authors show that activation of the PKR/eIF2α-P pathway exhibits anti-tumor effects in HER2+ cancer and is required for the response to Trastuzumab.

Image-based biomarkers for solid tumor quantification

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions

Lung cancer is the type of cancer that most often kills after the initial diagnosis. To aid the specialist in its diagnosis, temporal evaluation is a potential tool for analyzing indeterminate lesions, which may be benign or malignant, during treatment. With this goal in mind, a methodology is herein proposed for the analysis, quantification, and visualization of changes in lung lesions. This methodology uses a modified version of the quality threshold clustering algorithm to associate each voxel of the lesion to a cluster, and changes in the lesion over time are defined in terms of voxel moves to another cluster. In addition, statistical features are extracted for classification of the lesion as benign or malignant. To develop the proposed methodology, two databases of pulmonary lesions were used, one for malignant lesions in treatment (public) and the other for indeterminate cases (private). We determined that the density change percentage varied from 6.22% to 36.93% of lesion volume in the public database of malignant lesions under treatment and from 19.98% to 38.81% in the private database of lung nodules. Additionally, other inter-cluster density change measures were obtained. These measures indicate the degree of change in the clusters and how each of them is abundant in relation to volume. From the statistical analysis of regions in which the density changes occurred, we were able to discriminate lung lesions with an accuracy of 98.41%, demonstrating that these changes can indicate the true nature of the lesion. In addition to visualizing the density changes occurring in lesions over time, we quantified these changes and analyzed the entire set through volumetry, which is the technique most commonly used to analyze changes in pulmonary lesions.

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.

Using Response Evaluation Criteria in Solid Tumors in real-world evidence cancer research

Aim: Real-world evidence of charted treatment responses to cancer drug therapy was compared with medical record derived radiographic measurements of target lesions per Response Evaluation Criteria in Solid Tumors (RECIST). Materials & methods: 15 physicians treating 59 metastatic Merkel cell cancer (mMCC) patients contributed patient-level data. A comparison of medical record reported best response with radiographic measurements per RECIST of pre- and post-treatment target lesions. Results: RECIST response rates were significantly lower compared with medical record reported with a concordance of 43.2% (95% CI: 28.0–58.4%). Conclusion: Subjective assessment of tumor response collected via traditional chart abstraction may overestimate benefit and limit the potential role of real-world evidence in value-based care research. The use of target lesion measurements presents an attractive alternative that better aligns with trial results.

Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA

Understanding mechanisms of late/acquired cancer immunotherapy resistance is critical to improve outcomes; cellular immunotherapy trials offer a means to probe complex tumor-immune interfaces through defined T cell/antigen interactions. We treated two patients with metastatic Merkel cell carcinoma with autologous Merkel cell polyomavirus specific CD8+ T cells and immune-checkpoint inhibitors. In both cases, dramatic remissions were associated with dense infiltration of activated CD8+s into the regressing tumors. However, late relapses developed at 22 and 18 months, respectively. Here we report single cell RNA sequencing identified dynamic transcriptional suppression of the specific HLA genes presenting the targeted viral epitope in the resistant tumor as a consequence of intense CD8-mediated immunologic pressure; this is distinguished from genetic HLA-loss by its reversibility with drugs. Transcriptional suppression of Class I loci may underlie resistance to other immunotherapies, including checkpoint inhibitors, and have implications for the design of improved immunotherapy treatments.

Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Resisting RECIST-Uniformity Versus Clinical Validity

OBJECTIVES

The Response Evaluation Criteria in Solid Tumors (RECIST) International Working Group developed criteria for tumor response and progression to standardize radiological assessment in patients receiving chemotherapy in phase 2 trials. However, it is unclear whether the defined percentage change in tumor size and volume reflects true clinical benefit for the patient. The RECIST criteria were designed to improve objectivity in trials, but not to replace clinical decision making. The aim of this study was to understand clinicians' opinions about RECIST in current oncology practice.

METHODS

Using Web-based questionnaires, we investigated attitudes to the use of RECIST at a large comprehensive cancer center and in an international group of gynecologic cancer specialists through the Gynecologic Cancer InterGroup. The results reported here relate to the survey focusing on gynecologic cancer.

RESULTS

Sixty medical professionals from 13 countries responded to the survey. The majority of respondents worked at a tertiary or specialist cancer center (51; 86%). Overall, 66% of respondents felt RECIST increased trial objectivity and was a good measure of response. The majority of respondents (81%) reported that they infrequently challenged RECIST evaluation. Overall, 60% felt more than 10% of patients came off trial for clinical rather than radiological progression. In the context of a new small lesion, only 35% felt that should always be considered disease progression. The importance of both clinician and radiologist input was highlighted with nontarget progression. Nontarget progression and target progression were recognized as equally important for clinical decision making (72%).

CONCLUSIONS

RECIST is a key criterion for endpoint assessment in clinical trials with its value recognized by clinicians. However, this survey also highlights the practical limitations of RECIST. Disconnect can be seen between the radiological result and the clinical picture-learning from these patients is critical. Continued efforts to improve metrics assessing patient benefit in trials remains a priority.

Advanced imaging to predict response to chemotherapy in colorectal liver metastases - a systematic review

BACKGROUND

The assessment of colorectal liver metastases (CRLM) after treatment with chemotherapy is challenging due to morphological and/or functional change without changes in size. The aim of this review was to assess the value of FDG-PET, FDG-PET-CT, CT and MRI in predicting response to chemotherapy in CRLM.

METHODS

A systematic review was undertaken based on PRISMA statement. PubMed and Embase were searched up to October 2016 for studies on the accuracy of PET, PET-CT, CT and MRI in predicting RECIST or metabolic response to chemotherapy and/or survival in patients with CRLM. Articles evaluating the assessment of response after chemotherapy were excluded.

RESULTS

Sixteen studies met the inclusion criteria and were included for further analysis. Study results were available for 6 studies for FDG-PET(-CT), 6 studies for CT and 9 studies for MRI. Generally, features predicting RECIST or metabolic response often predicted shorter survival. The ADC (apparent diffusion coefficient, on MRI) seems to be the most promising predictor of response and survival. In CT-related studies, few attenuation-related parameters and texture features show promising results. In FDG-PET(-CT), findings were ambiguous.

CONCLUSION

Radiological data on the prediction of response to chemotherapy for CRLM is relatively sparse and heterogeneous. Despite that, a promising parameter might be ADC. Second, there seems to be a seemingly counterintuitive correlation between parameters that predict a good response and also predict poor survival.

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

Kinetic change of serum carcinoembryonic antigen can early predict progression in patients with metastatic non-small cell lung cancer during maintenance therapy with bevacizumab plus pemetrexed

In this retrospective study, we investigated whether the kinetic change of serum carcinoembryonic antigen (CEA) levels can be an early indicator for the progression in metastatic non-small cell lung cancer (NSCLC) patients during maintenance therapy with bevacizumab plus pemetrexed. Ten patients diagnosed with metastatic lung adenocarcinoma who received a first-line therapy including bevacizumab-based chemotherapy and a following maintenance therapy including bevacizumab plus pemetrexed from June 2015 to October 2016 were recruited in this study. During the maintenance treatment, patients’ CEA levels all elevated at or after the first cycle of maintenance treatment with a median CEA elevation-free survival time as 17.7 days, which was far more shorter than the median progression-free survival time evaluated by CT imaging specially for maintenance treatment (102.2 days). Before the disease progressed, the values of CEA increased steadily for several cycles with the response evaluation still as stable disease, indicating that the changes of CEA level would be earlier and more sensitive for detection of progression. The CEA kinetic was calculated with a mean of 9.6451 and a median of 8.0135, which sensitively reflected the increasing rate of CEA levels at an early stage. Our study showed that the kinetic change of CEA could be an early predictor for the progression in metastatic NSCLC patients during maintenance therapy.

Comparison of One-Dimensional and Volumetric Computed Tomography Measurements of Injected-Water Phantoms

The goal of this study was to compare volumetric analysis in computed tomography (CT) with the length measurement prescribed by the Response Evaluation Criteria in Solid Tumors (RECIST) for a system with known mass and unknown shape. We injected 2 mL to 4 mL of water into vials of sodium polyacrylate and into disposable diapers. Volume measurements of the sodium polyacrylate powder were able to predict both mass and proportional changes in mass within a 95 % prediction interval of width 12 % and 16 %, respectively. The corresponding figures for RECIST were 102 % and 82 %.

Near infrared fluorescent imaging of choline kinase alpha expression and inhibition in breast tumors

Choline kinase alpha (ChoKα) overexpression is associated with an aggressive tumor phenotype. ChoKα inhibitors induce apoptosis in tumors, however validation of their specificity is difficult in vivo. We report the use of optical imaging to assess ChoKα status in cells and in vivo using JAS239, a carbocyanine-based ChoKα inhibitor with inherent near infrared fluorescence. JAS239 attenuated choline phosphorylation and viability in a panel of human breast cancer cell lines. Antibody blockade prevented cellular retention of JAS239 indicating direct interaction with ChoKα independent of the choline transporters and catabolic choline pathways. In mice bearing orthotopic MCF7 breast xenografts, optical imaging with JAS239 distinguished tumors overexpressing ChoKα from their empty vector counterparts and delineated tumor margins. Pharmacological inhibition of ChoK by the established inhibitor MN58b led to a growth inhibition in 4175-Luc+ tumors that was accompanied by concomitant reduction in JAS239 uptake and decreased total choline metabolite levels as measured using magnetic resonance spectroscopy. At higher therapeutic doses, JAS239 was as effective as MN58b at arresting tumor growth and inducing apoptosis in MDA-MB-231 tumors, significantly reducing tumor choline below baseline levels without observable systemic toxicity. These data introduce a new method to monitor therapeutically effective inhibitors of choline metabolism in breast cancer using a small molecule companion diagnostic.

Prognostic and predictive factors in patients with metastatic or recurrent cervical cancer treated with platinum-based chemotherapy

BACKGROUND

Recognizing resistance or susceptibility to the current standard cisplatin and paclitaxel treatment could improve therapeutic outcomes of metastatic or recurrent cervical cancer.

METHODS

Forty-five tissue samples from patients participating in a phase II trial of cisplatin and ifosfamide, with or without paclitaxel were collected for retrograde analysis. Immunohistochemistry and genotyping was performed to test ERCC1, III β-tubulin, COX-2, CD4, CD8 and ERCC1 (C8092A and N118 N) and MDR1 (C3435T and G2677 T) gene polymorphisms, as possible predictive and prognostic markers. Results were statistically analyzed and correlated with patient characteristics and outcomes.

RESULTS

Patients with higher levels of ERCC1 expression had shorter PFS and OS than patients with low ERCC1 expression (mPFS:5.1 vs 10.2 months, p = 0.027; mOS:10.5 vs. 21.4 months, p = 0.006). Patients with TT in the site of ERCC1 N118 N and GT in the site of MDR1 G2677 T polymorphisms had significantly longer PFS (p = 0.006 and p = 0.027 respectively). ERCC1 expression and the ERCC1 N118 N polymorphism remained independent predictors of PFS. Interestingly, high III beta tubulin expression was associated with chemotherapy resistance and fewer responses [5/20 (25%)] compared to lower III β-tubulin expression [15/23 (65.2%)] (p = 0.008). Finally, ΙΙΙ β-tubulin levels and chemotherapy regimen were independent predictors of response to treatment.

CONCLUSIONS

ERCC1 expression proved to be a significant prognostic factor for survival in our metastatic or recurrent cervical cancer population treated with cisplatin based chemotherapy. ERCC1 N118 N and MDR1 G2677 T polymorphism also proved of prognostic significance for disease progression, while overexpression of III β-tubulin was positively correlated with chemotherapy resistance.

Vaccination with poly(IC:LC) and peptide-pulsed autologous dendritic cells in patients with pancreatic cancer

BACKGROUND

Dendritic cells (DCs) enhance the quality of anti-tumor immune response in patients with cancer. Thus, we posit that DC-based immunotherapy, in conjunction with toll-like receptor (TLR)-3 agonist poly-ICLC, is a promising approach for harnessing immunity against metastatic or locally advanced unresectable pancreatic cancer (PC).

METHODS

We generated autologous DCs from the peripheral blood of HLA-A2⁺ patients with PC. DCs were pulsed with three distinct A2-restricted peptides: 1) human telomerase reverse transcriptase (hTERT, TERT572Y), 2) carcinoembryonic antigen (CEA; Cap1-6D), and 3) survivin (SRV.A2). Patients received four intradermal injections of 1 × 10⁷ peptide-pulsed DC vaccines every 2 weeks (Day 0, 14, 28, and 42). Concurrently, patients received intramuscular administration of Poly-ICLC at 30 μg/Kg on vaccination days (i.e., day 0, 14, 28, and 42), as well as on days 3, 17, 21, 31, 37, and 45. Our key objective was to assess safety and feasibility. The effect of DC vaccination on immune response was measured at each DC injection time point by enumerating the phenotype and function of patient T cells.

RESULTS

Twelve patients underwent apheresis: nine patients with metastatic disease, and three patients with locally advanced unresectable disease. Vaccines were successfully manufactured from all individuals. We found that this treatment was well-tolerated, with the most common symptoms being fatigue and/or self-limiting flu-like symptoms. Among the eight patients who underwent imaging on day 56, four patients experienced stable disease while four patients had disease progression. The median overall survival was 7.7 months. One patient survived for 28 months post leukapheresis. MHC class I -tetramer analysis before and after vaccination revealed effective generation of antigen-specific T cells in three patients with stable disease.

CONCLUSION

Vaccination with peptide-pulsed DCs in combination with poly-ICLC is safe and induces a measurable tumor specific T cell population in patients with advanced PC.

TRIAL REGISTRATION

NCT01410968 ; Name of registry: clinicaltrials.gov; Date of registration: 08/04/2011).

Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer

Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.

Statistical tools for the temporal analysis and classification of lung lesions

BACKGROUND AND OBJECTIVE

Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant.

METHODS

We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time. We employ the Jensen divergence and hypothesis test locally to verify voxel-to-voxel changes, and globally to capture changes in lesion volumes.

RESULTS

For the local hypothesis test, we determine that the change in density varies by between 3.84 and 40.01% of the lesion volume in a public database of malignant lesions under treatment, and by between 5.76 and 35.43% in a private database of benign lung nodules. From the texture analysis of regions in which the density changes occur, we are able to discriminate lung lesions with an accuracy of 98.41%, which shows that these changes can indicate the true nature of the lesion.

CONCLUSION

In addition to the visual aspects of the density changes occurring in the lesions over time, we quantify these changes and analyze the entire set using volumetry. In the case of malignant lesions, large b-divergence values are associated with major changes in lesion volume. In addition, this occurs when the change in volume is small but is associated with significant changes in density, as indicated by the histogram divergence. For benign lesions, the methodology shows that even in cases where the change in volume is small, a change of density occurs. This proves that even in lesions that appear stable, a change in density occurs.

Diffusion MRI in early cancer therapeutic response assessment

Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. Copyright © 2016 John Wiley & Sons, Ltd.

Radiological imaging markers predicting clinical outcome in patients with metastatic colorectal carcinoma treated with regorafenib: post hoc analysis of the CORRECT phase III trial (RadioCORRECT study)

OBJECTIVE

To identify imaging markers predicting clinical outcomes to regorafenib in metastatic colorectal carcinoma (mCRC).

METHODS

The RadioCORRECT study is a post hoc analysis of a cohort of patients with mCRC treated within the phase III placebo-controlled CORRECT trial of regorafenib. Baseline and week 8 contrast-enhanced CT were used to assess response by RECIST 1.1, changes in the sum of target lesion diameters (ΔSTL), lung metastases cavitation and liver metastases density. Primary and secondary objectives were to develop ex novo univariable and multivariable models to predict overall survival (OS) and progression-free survival (PFS), respectively.

RESULTS

202 patients were enrolled, 134 (66.3%) treated with regorafenib and 68 (33.7%) with placebo. In the univariate analysis, PFS predictors were lung metastases cavitation at baseline (HR 0.50, 95% CI 0.27 to 0.92, p=0.03) and at week 8 (HR 0.58, 95% CI 0.36 to 0.93, p=0.02). Baseline cavitation (HR 0.23, 95% CI 0.08 to 0.66, p=0.007), RECIST 1.1 (HR 0.23, 95% CI 0.14 to 0.4, p <0.0001) and ΔSTL (HR 1.16, 95% CI 1.06 to 1.27, p=0.002) predicted OS. We found an increase of 9% of diameter as the best threshold for discriminating OS (HR 2.64, 95% CI 1.61 to 4.34, p <0.001). In the multivariate analysis, baseline and week 8 cavitation remained significant PFS predictors. Baseline cavitation, RECIST 1.1 and ΔSTL remained predictors of OS in exploratory multivariable models. Assessment of liver metastases density did not predict clinical outcome.

CONCLUSIONS

RECIST 1.1 and ΔSTL predict favourable outcome to regorafenib. In contrast to liver metastases density that failed to be a predictor, lung metastases cavitation represents a novel radiological marker of favourable outcome that deserves consideration.

Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases

OBJECTIVES

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth.

MATERIALS AND METHODS

For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE).

RESULTS

SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold.

KEY POINTS

• The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI

PURPOSE

To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI.

METHODS

A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K(trans) map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d1 and d2 of K(trans) and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day.

RESULTS

The tumor mean K(trans) and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of K(trans) map were 87.5% and 100% at first and second post-treatment scan days, respectively.

CONCLUSIONS

As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

Pretreatment tumor volume as a prognostic factor in metastatic colorectal cancer treated with selective internal radiation to the liver using yttrium-90 resin microspheres

BACKGROUND

Yttrium-90 (⁹⁰Y)-resin microspheres can prolong intrahepatic disease control and improve overall survival (OS) in patients with metastatic colorectal cancer (CRC). Prognostic factors for improved outcomes in patients undergoing selective internal radiation therapy (SIRT) have been studied, but the relationship between pre-SIRT liver tumor volume and outcomes has not well described.

METHODS

We retrospectively reviewed the records of patients with metastatic CRC who were treated at our institution with ⁹⁰Y-resin microspheres. Each patient underwent either MR or CT imaging of the liver with intravenous (IV) contrast before and within ~2-3 months after SIRT. Imaging data were transferred into our treatment planning system. Each metastatic liver lesion was contoured, and the volume of each lesion was summed to determine the total liver tumor volume at a given time point. We evaluated whether pretreatment liver tumor volume was related to OS. We also evaluated the relationship between pre-SIRT tumor volume and radiographic treatment response by either unidimensional Response Evaluation Criteria in Solid Tumors (RECIST) or three-dimensional volumetric criteria.

RESULTS

We included 60 patients with a median age of 59 years (range, 38-97 years); 60% of patients received sequential lobar treatment. The median number of chemotherapy cycles received prior to SIRT was 2. Median follow-up from first SIRT was 8.9 months. Pre- and post-SIRT tumor volumes were primarily calculated on CT (87%). The median pre-SIRT tumor volume was 77 cc (range, 4.5-2,170.4 cc). The median intervals between the first SIRT and the first, second, and third follow-up scans were 2.2, 4.4, and 7.7 months, respectively. No patient experienced a radiographic complete response. Pretreatment volume was a significant predictor for estimating the odds of a patient having stable disease or partial response using volumetric response criteria at first (P=0.016), second (P=0.023), and third (P=0.015) follow-ups. For each unit increase in log volume, a patient's odds of having a stable or partial response were 0.57, 0.63, and 0.61 times as likely at first, second, and third follow-up, respectively. OS was not significantly associated with pretreatment tumor volume.

CONCLUSIONS

Patients with metastatic CRC with larger overall pretreatment liver tumor volumes, regardless of number of individual liver lesions, are less likely to have radiographic evidence of stable disease or partial response following SIRT using volumetric response criteria. However, pretreatment volume was not significantly associated with OS, and thus SIRT should be considered for patients with larger pretreatment volumetric tumor burden.