Predicting pathologic tumor response to chemoradiotherapy with histogram distances characterizing longitudinal changes in 18F-FDG uptake patterns

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model

OBJECTIVES

To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.

METHODS

The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers.

RESULTS

A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers.

CONCLUSION

The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients.

CLINICAL RELEVANCE STATEMENT

The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed.

KEY POINTS

• Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

BACKGROUND

Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy.

MATERIALS AND METHODS

PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented.

RESULTS

Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature.

CONCLUSIONS

Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Value of [18F]FDG PET/CT radiomic parameters in the context of response to chemotherapy in advanced cervical cancer

The first-order statistical (FOS) and second-order texture analysis on basis of Gray-Level Co-occurence Matrix (GLCM) were obtained to assess metabolic, volumetric, statistical and radiomic parameters of cervical cancer in response to chemotherapy, recurrence and age of patients. The homogeneous group of 83 patients with histologically confirmed IIIC1-IVB stage cervical cancer were analyzed, retrospectively. Before and after chemotherapy, the advancement of the disease and the effectiveness of the therapy, respectively, were established using [¹⁸F] FDG PET/CT imaging. The statistically significant differences between pre- and post-therapy parameters were observed for SUV_max, SUV_mean, TLG, MTV, asphericity (ASP, p = 0.000, Z > 0), entropy (E, p = 0.0000), correlation (COR, p = 0.0007), energy (En, p = 0.000) and homogeneity (H, p = 0.0018). Among the FOS parameters, moderate correlation was observed between pre-treatment coefficient of variation (COV) and patients' recurrence (R = 0.34, p = 0.001). Among the GLCM textural parameters, moderate positive correlation was observed for post-treatment contrast (C) with the age of patients (R = 0.3, p = 0.0038) and strong and moderate correlation was observed in the case of En and H with chemotherapy response (R = 0.54 and R = 0.46, respectively). All correlations were statistically significant. This study indicates the remarkable importance of pre- and post-treatment [¹⁸F] FDG PET statistical and textural GLCM parameters according to prediction of recurrence and chemotherapy response of cervical cancer patients.

Role of (F-18) Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in the Prediction of Response to Neoadjuvant Therapy in Esophageal Cancer: Correlation with Pathological Response and Survival

Purpose

The purpose of this study is to assess the correlation between metabolic response with fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and pathological response in patients with locally advanced esophageal cancer treated with neoadjuvant chemoradiotherapy and to study FDG-PET parameters for the prediction of pathological response and outcome.

Methods

Twenty-five patients with locally advanced esophageal cancer underwent two FDG-PET/CT scans for initial staging and after neoadjuvant chemoradiotherapy. FDG uptake in the primary tumor was calculated in both scans (SUVmax, SULpeak, and TLG). Metabolic response was assessed according to the reduction of PET parameters: complete response (mCR = 100%), partial response (mPR ≥50%), and no response (mNR ≤50%). Pathological response was also classified as complete (pCR), partial (pPR), or no response (pNR). Patients were followed up (range, 8-99 months) determining free-disease interval (FDI) and overall survival (OS).

Results

Two patients were excluded due to exitus for nonesophageal-related causes. The metabolic response was observed in 18/23 remaining patients (3mCR, 15 mPR), of which 12/18 patients showed a pathological response (3 pCR, 9 pPR). A major discrepancy was observed in 2 mNR patients who achieved pPR. FDI and OS were longer in patients with metabolic response than nonresponders, but no statistical difference was found. No significant correlation was found between PET parameters and pathological response, FDI, and OS.

Conclusions

FDG-PET/CT is a useful technique to assess response to neoadjuvant chemoradiotherapy in esophageal cancer. Although in this preliminary study, no correlation between metabolic and pathologic response was found and no statistical differences between responders and nonresponders were observed, a tendency of longer FDI and OS was apparently found in responders patients.

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

PURPOSE

Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.

METHODS

A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.

RESULTS

Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings.

CONCLUSION

A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.

Receptor-interacting protein in malignant digestive neoplasms

A deep and comprehensive understanding of factors that contribute to cancer initiation, progression, and evolution is of essential importance. Among them, the serine/threonine and tyrosine kinase-like kinases, also known as receptor interacting proteins (RIPs) or receptor interacting protein kinases (RIPKs), is emerging as important tumor-related proteins due to its complex regulation of cell survival, apoptosis, and necrosis. In this review, we mainly review the relevance of RIP to various malignant digestive neoplasms, including esophageal cancer, gastric cancer, colorectal cancer, hepatocellular carcinoma, gallbladder cancer, cholangiocarcinoma, and pancreatic cancer. Consecutive research on RIPs and its relationship with malignant digestive neoplasms is required, as it ultimately conduces to the etiology and treatment of cancer.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV_max). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.

Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer

The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer.

Challenges and Promises of PET Radiomics

PURPOSE

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.

METHODS AND MATERIALS

Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.

RESULTS

In relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.

CONCLUSIONS

Although at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

Report on the development and application of PET/CT in mainland China

PURPOSE

To examine the development and application of systems combining positron emission and x-ray-computed tomography systems (PET/CTs) in mainland China.

METHODS

Using a questionnaire, we surveyed Chinese medical institutions on a variety topics relating to their PET/CT systems and its use. The respondents had PET/CTs installed and in clinical use before 31 December 2015. We examined the clinical scenarios to which Chinese PET/CTs were applied by reviewing the related Chinese and international literature from the start of 1995 to the end of 2013; these papers were found by searching the Wanfang and PubMed databases, respectively. The data were then classified and analyzed statistically.

RESULTS

At the end of 2015, there were 240 PET/CTs and 101 medical cyclotrons in mainland China. The total number of PET studies performed in 2015 was 469,364. The main clinical applications of PET were found to be diagnostic fludeoxyglucose (¹⁸F-FDG) imaging and oncological imaging. A minority of PET/CT studies were performed using ¹¹C-choline and other imaging agents. The number of papers relating to clinical use of PET/CT in mainland China increased each year over the period of study, in both the Chinese and international literature. Despite this progress, important problems were also apparent, including unbalanced regional development and the limited quality of the research.

CONCLUSIONS

This study provides detailed information for understanding the development PET/CT technology in mainland China, along with its geographical distribution and clinical application. It may thus prove a useful reference for all those involved in planning the future of PET/CT in China.

PET/Computed Tomography Scanning and Precision Medicine: Esophageal Cancer

Esophageal cancer commonly has a poor prognosis, which requires an accurate diagnosis and early treatment to improve outcome. Other modalities for staging, such as endoscopic ultrasound imaging and computed tomography (CT) scans, have a role in diagnosis and staging. However, PET with fluorine-18 fluoro-2-deoxy-d-glucose/CT (FDG PET/CT) scanning allows for improved detection of distant metastatic disease and can help to prevent unnecessary interventions that would increase morbidity. FDG PET/CT scanning is valuable in the neoadjuvant chemotherapy assessment and predicting survival outcomes subsequent to surgery. FDG PET/CT scanning detects recurrent disease and metastases in follow-up.

Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF  =  9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne's adaptive thresholding method (DSI  =  0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier

Purpose

In oncology, texture features extracted from positron emission tomography with 18-fluorodeoxyglucose images (FDG-PET) are of increasing interest for predictive and prognostic studies, leading to several tens of features per tumor. To select the best features, the use of a random forest (RF) classifier was investigated.

Methods

Sixty-five patients with an esophageal cancer treated with a combined chemo-radiation therapy were retrospectively included. All patients underwent a pretreatment whole-body FDG-PET. The patients were followed for 3 years after the end of the treatment. The response assessment was performed 1 month after the end of the therapy. Patients were classified as complete responders and non-complete responders. Sixty-one features were extracted from medical records and PET images. First, Spearman’s analysis was performed to eliminate correlated features. Then, the best predictive and prognostic subsets of features were selected using a RF algorithm. These results were compared to those obtained by a Mann-Whitney U test (predictive study) and a univariate Kaplan-Meier analysis (prognostic study).

Results

Among the 61 initial features, 28 were not correlated. From these 28 features, the best subset of complementary features found using the RF classifier to predict response was composed of 2 features: metabolic tumor volume (MTV) and homogeneity from the co-occurrence matrix. The corresponding predictive value (AUC = 0.836 ± 0.105, Se = 82 ± 9%, Sp = 91 ± 12%) was higher than the best predictive results found using the Mann-Whitney test: busyness from the gray level difference matrix (P < 0.0001, AUC = 0.810, Se = 66%, Sp = 88%). The best prognostic subset found using RF was composed of 3 features: MTV and 2 clinical features (WHO status and nutritional risk index) (AUC = 0.822 ± 0.059, Se = 79 ± 9%, Sp = 95 ± 6%), while no feature was significantly prognostic according to the Kaplan-Meier analysis.

Conclusions

The RF classifier can improve predictive and prognostic values compared to the Mann-Whitney U test and the univariate Kaplan-Meier survival analysis when applied to several tens of features in a limited patient database.

The emerging field of radiomics in esophageal cancer: current evidence and future potential

'Radiomics' is the name given to the emerging field of extracting additional information from standard medical images using advanced feature analysis. This innovative form of quantitative image analysis appears to have future potential for clinical practice in patients with esophageal cancer by providing an additional layer of information to the standard imaging assessment. There is a growing body of evidence suggesting that radiomics may provide incremental value for staging, predicting treatment response, and predicting survival in esophageal cancer, for which the current work-up has substantial limitations. This review outlines the available evidence and future potential for the application of radiomics in the management of patients with esophageal cancer. In addition, an overview of the current evidence on the importance of reproducibility of image features and the substantial influence of varying smoothing scales, quantization levels, and segmentation methods is provided.

Positron emission tomography/computerized tomography for tumor response assessment-a review of clinical practices and radiomics studies

Even with recent advances in cancer diagnosis and therapy, treatment outcomes for many cancers remain dismal. Patients often show different response to the same therapy regimen, supporting the development of personalized medicine. 18F-FDG PET/CT has been used routinely in the assessment of tumor response, in prediction of outcomes, and in guiding personalized treatment. These assessments are mainly based on physician's subjective or semi-quantitative evaluation. Recent development in Radiomics provides a promising objective way for tumor response assessment, which uses computerized tools to extract a large number of image features that capture additional information not currently used in clinic that has prognostic value. In this review, we summarized the clinical use of PET/CT and the PET/CT Radiomics studies for tumor response assessment. Finally, we discussed some challenges and future perspectives.

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R(2) = 0.83) than the 4 GLCM parameters (R(2) = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications.

Pre-Chemoradiotherapy FDG PET/CT cannot Identify Residual Metabolically-Active Volumes within Individual Esophageal Tumors

Objective

To study whether subvolumes with a high pre-chemoradiotherapy (CRT) FDG uptake could identify residual metabolically-active volumes (MAVs) post-CRT within individual esophageal tumors. Accurate identification will allow simultaneous integrated boost to these subvolumes at higher risk to improve clinical outcomes.

Methods

Twenty patients with esophageal cancer were treated with CRT plus surgery and underwent FDG PET/CT scans before and after CRT. The two scans were rigidly registered. Seven MAVs pre-CRT and four MAVs post-CRT within a tumor were defined with various SUV thresholds. The similarity and proximity between the MAVs pre-CRT and post-CRT were quantified with three metrics: fraction of post-CRT MAV included in pre-CRT MAV, volume overlap and centroid distance.

Results

Eight patients had no residual MAV. Six patients had local residual MAV (SUV ≥2.5 post-CRT) within or adjoining the original MAV (SUV ≥2.5 pre-CRT). On average, less than 65% of any post-CRT MAVs was included in any pre-CRT MAVs, with a low volume overlap <45%, and large centroid distance >8.6 mm. In general, subvolumes with higher FDG-uptake pre-CRT or post-CRT had lower volume overlap and larger centroid distance. Six patients had new distant MAVs that were determined to be inflammation from radiation therapy.

Conclusions

Pre-CRT PET/CT cannot reliably identify the residual MAVs within individual esophageal tumors. Simultaneous integrated boost to subvolumes with high FDG uptake pre-CRT may not be feasible.

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

Current cancer therapy strategy is mostly population based, however, there are large differences in tumour response among patients. It is therefore important for treating physicians to know individual tumour response. In recent years, many studies proposed the use of computerized positron emission tomography/CT image analysis in the evaluation of tumour response. Results showed that computerized analysis overcame some major limitations of current qualitative and semiquantitative analysis and led to improved accuracy. In this review, we summarize these studies in four steps of the analysis: image registration, tumour segmentation, image feature extraction and response evaluation. Future works are proposed and challenges described.