Repeated positron emission tomography-computed tomography and perfusion-computed tomography imaging in rectal cancer: fluorodeoxyglucose uptake corresponds with tumor perfusion

Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy Prediction

(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma.

Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.

Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer

PURPOSE

The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.

METHODS AND MATERIALS

Three centers using different MR scanners were involved in this retrospective study. The first center recruited 162 patients for model training, and the second and third centers provided 34 plus 25 patients for external validation. Patients provided written consent. Accrual period was from May 2008 to December 2014. After surgery pathologic response was defined. T2-weighted MR scans acquired before chemoradiation therapy (CRT) were used for analysis addressed on primary lesions. Images were pre-processed using Laplacian of Gaussian (LoG) filter with multiple σ, and first order intensity histogram-based features (kurtosis, skewness, and entropy) were extracted. Features selection was performed using Mann-Whitney test. Tumor staging (cT, cN) was added to build a logistic regression model and predict pCR. Model performance was evaluated with internal and external validation using area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration with Hosmer-Lemeshow test. The linear cross-correlation matrix (Pearson's coefficient) and the variance inflation factor (VIF) were used to check the correlation and the co-linearity among the final predictors. The amount of the information added through the radiomics features was estimated by using the DeLong's test, and the impact of pCR and the final predictors on survival outcomes were evaluated through the Kaplan-Meier curves by using the log-rank test and the multivariate Cox model.

RESULTS

Candidate-to-analysis features were skewness (σ = 0.485, P value = .01) and entropy (σ = 0.344, P value < .05). Logistic regression analysis showed as significant covariates cT (P value < .01), skewness-σ = 0.485 (P value = .01), and entropy-σ = 0.344 (P value < .05). Model AUCs were 0.73 (internal) and 0.75 (external).

CONCLUSIONS

This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging.

Pilot study of combined FDG-PET and dynamic contrast-enhanced CT of locally advanced cervical carcinoma before and during concurrent chemoradiotherapy suggests association between changes in tumor blood volume and treatment response

Modern PET/CT radiotherapy simulators offer FDG-PET and dynamic contrast-enhanced (DCE) CT imaging for combined volumetric assessment of tumor metabolism and perfusion. However, the clinical utility of such assessment has not been clearly defined. Thus, in a prospective longitudinal study of primary cervical tumors treated with concurrent chemoradiotherapy (CCRT) we evaluated: (1) whether PET and perfusion parameters correlate or provide complementary information; (2) what imaging changes occur during CCRT; and (3) whether any parameters are predictive of treatment response as assessed by PET/CT 3 months posttherapy. FDG-PET/CT and DCE-CT scans were performed on 21 patients prior to and during CCRT. Coregistered volumetric parametric maps of standardized uptake value (SUV) measures and perfusion parameters blood flow (BF), blood volume (BV), and permeability were generated. Summary statistics for these parameters and their changes were calculated within the metabolic tumor volume (MTV). Correlations between SUV and BF/BV/permeability on local and global bases were assessed with Pearson's coefficient r. MTV, maximum SUV, and mean SUV decreased significantly between the pre- and during-treatment time points, while mean BV and permeability increased significantly. Global correlations between mean BF/BV/permeability and mean SUV values (-.15 < r < .29) were at most moderate. An increase in mean tumor BV during treatment was significantly correlated with complete metabolic response on 3-month posttreatment PET/CT. Weak correlations of SUV and perfusion parameters suggest a complementary role of FDG-PET and DCE-CT for tumor characterization. The association between relative change in mean BV and outcome suggests a potential role for DCE-CT in early evaluation of cervical tumor response to chemoradiotherapy.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ^©RSNA, 2018.

Sequential PET/CT with [18F]-FDG Predicts Pathological Tumor Response to Preoperative Short Course Radiotherapy with Delayed Surgery in Patients with Locally Advanced Rectal Cancer Using Logistic Regression Analysis

Previous studies indicate that FDG PET/CT may predict pathological response in patients undergoing neoadjuvant chemo-radiotherapy for locally advanced rectal cancer (LARC). Aim of the current study is evaluate if pathological response can be similarly predicted in LARC patients after short course radiation therapy alone. Methods: Thirty-three patients with cT2-3, N0-2, M0 rectal adenocarcinoma treated with hypo fractionated short course neoadjuvant RT (5x5 Gy) with delayed surgery (SCRTDS) were prospectively studied. All patients underwent 3 PET/CT studies at baseline, 10 days from RT end (early), and 53 days from RT end (delayed). Maximal standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and total lesion glycolysis (TLG) of the primary tumor were measured and recorded at each PET/CT study. We use logistic regression analysis to aggregate different measures of metabolic response to predict the pathological response in the course of SCRTDS. Results: We provide straightforward formulas to classify response and estimate the probability of being a major responder (TRG1-2) or a complete responder (TRG1) for each individual. The formulas are based on the level of TLG at the early PET and on the overall proportional reduction of TLG between baseline and delayed PET studies. Conclusions: This study demonstrates that in the course of SCRTDS it is possible to estimate the probabilities of pathological tumor responses on the basis of PET/CT with FDG. Our formulas make it possible to assess the risks associated to LARC borne by a patient in the course of SCRTDS. These risk assessments can be balanced against other health risks associated with further treatments and can therefore be used to make informed therapy adjustments during SCRTDS.

The predictive value of 18F-FDG PET/CT for assessing pathological response and survival in locally advanced rectal cancer after neoadjuvant radiochemotherapy

PURPOSE

To evaluate whether metabolic changes in the primary tumour during and after preoperative radiochemotherapy (RCT) can predict the histopathological response in patients with locally advanced rectal cancer as well as disease-free survival (DFS) and overall survival (OS).

METHODS

Consecutive patients with cT2-4 N0-2 rectal adenocarcinoma were included. (18)F-FDG PET/CT was performed at baseline, at the end of the second week of RCT (early PET/CT) and before surgery (late PET/CT). The PET/CT results were compared with histopathological data (ypT0 N0 vs. ypT1-4 N0-2 as well as TRG1 vs.TRG2-5) and survival.

RESULTS

The study included 126 patients. Among 124 patients in whom TNM classification was available, 28 (22.6 %) were ypT0 N0, and among all 126 patients, 31 (24.6 %) were TRG1. The areas under the curve of the early response index (RI) for identifying non-complete pathological response (non-cPR) were 0.74 (95 % CI 0.61 - 0.87) for ypT1-4 N0-2 patients and 0.75 (95 % CI 0.62 - 0.88) for TRG2-5 patients. The optimal cut-off for differentiating patients with non-cPR and cPR was found to be a reduction of 61.2 % (83.1 % sensitivity and 65 % specificity in ypT1-4 N0-2 patients; 85.4 % sensitivity and 65.2 % specificity in TRG2-5 patients). The optimal cut-off for late RI could not be found. The qualitative analysis of images obtained after RCT demonstrated 81.5 % sensitivity and 61.3 % specificity in predicting TRG2-5. After a median follow-up of 68 months, the low number of patients with local/distant recurrence or who had died did not allow the value of PET/CT for predicting DFS and OS to be calculated.

CONCLUSION

The early assessment of response to RCT by (18)F-FDG PET/CT can predict non-cPR allowing practical modification of preoperative treatment. Conversely, late RI is not sufficiently accurate for guiding the decision as to whether local excision or even observation is appropriate in an individual patient. Qualitative analysis of late PET/CT images is also not sensitive enough alone to rule out the presence of residual disease.

Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer

METHODS

Thirty patients with proven colorectal cancer prospectively underwent integrated 18F-FDG PET/DCE-CT to assess the metabolic-flow phenotype. Both CT blood flow parametric maps and PET images were analyzed. Correlations between PET heterogeneity and perfusion CT were assessed by Spearman's rank correlation analysis.

RESULTS

Blood flow visualization provided by DCE-CT images was significantly correlated with 18F-FDG PET metabolically active tumor volume as well as with uptake heterogeneity for patients with stage III/IV tumors (|ρ|:0.66 to 0.78; p-value<0.02).

CONCLUSION

The positive correlation found with tumor blood flow indicates that intra-tumor heterogeneity of 18F-FDG PET accumulation reflects to some extent tracer distribution and consequently indicates that 18F-FDG PET intra-tumor heterogeneity may be associated with physiological processes such as tumor vascularization.

Integrated ¹⁸F-FDG PET/perfusion CT for the monitoring of neoadjuvant chemoradiotherapy in rectal carcinoma: correlation with histopathology

PURPOSE

The aim of this study was to prospectively monitor changes in the flow-metabolic phenotype (ΔFMP) of rectal carcinoma (RC) after neoadjuvant chemoradiotherapy (CRT) and to evaluate whether ΔFMP of RC correlate with histopathological prognostic factors including response to CRT.

METHODS

Sixteen patients with RC (12 men, mean age 60.7 ± 12.8 years) underwent integrated (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/perfusion CT (PET/PCT), followed by neoadjuvant CRT and surgery. In 13 patients, PET/PCT was repeated after CRT. Perfusion [blood flow (BF), blood volume (BV), mean transit time (MTT)] and metabolic [maximum and mean standardized uptake values (SUVmax, SUVmean)] parameters as well as the FMP (BF × SUVmax) were determined before and after CRT by two independent readers and correlated to histopathological prognostic factors of RC (microvessel density, necrosis index, regression index, vascular invasion) derived from resected specimens. The diagnostic performance of ΔFMP for prediction of treatment response was determined.

RESULTS

FMP significantly decreased after CRT (p < 0.001), exploiting higher changes after CRT as compared to changes of perfusion and metabolic parameters alone. Before CRT, no significant correlations were found between integrated PET/PCT and any of the histopathological parameters (all p > 0.05). After CRT, BV and SUVmax correlated positively with the necrosis index (r = 0.67/0.70), SUVmax with the invasion of blood vessels (r = 0.62) and ΔFMP with the regression index (r = 0.88; all p < 0.05). ΔFMP showed high accuracy for prediction of histopathological response to CRT (AUC 0.955, 95 % confidence interval 0.833-1.000, p < 0.01) using a cut-off value of -75%.

CONCLUSION

In RC, ΔFMP derived from integrated (18)F-FDG PET/PCT is useful for monitoring the effects of neoadjuvant CRT and allows prediction of histopathological response to CRT.