An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results

Prone versus Supine FDG PET/CT in the Staging of Breast Cancer

Supine [18F]Fluorodeoxyglucose (FDG) positron emission technology/computed tomography (PET/CT) is a commonly used modality for the initial staging of breast cancer, and several previous studies have shown superior sensitivity and specificity of prone FDG PET/CT in comparison to its supine counterpart. This retrospective study included 25 females with breast cancer referred for staging. They underwent supine FDG PET/CT followed by prone FDG PET/CT. The outcomes were: number of primary breast lesions, anatomical site of FDG-avid lymph nodes (LNs), and number and type of bone lesions, with SUVmax of all corresponding parameters. Performance was superior in prone acquisition compared to supine acquisition, with the respective results: 29 vs. 22 breast tumor lesions detected, 62 vs. 27 FDG-avid axillary LNs detected, sensitivity of 68% vs. 57%, specificity of 64% vs. 53%. The detection rate of axillary LNs in the prone position was significantly higher (p = 0.001). SUVmax for breast tumor lesions (p = 0.000) and number of detected axillary LNs (p = 0.002) were significantly higher in prone acquisition. Five patients were upstaged after experts read the prone acquisition. Prone FDG PET/CT acquisition is a promising technique in detecting primary breast lesions and metastatic LNs possibly missed in supine acquisition, which may lead to change in patient staging and management.

Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Experimental Models as Refined Translational Tools for Breast Cancer Research

Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

Correlation Between 18F-FDG PET/CT Findings and BI-RADS Assessment Using Ultrasound in the Evaluation of Breast Lesions: A Multicenter Study

RATIONALE AND OBJECTIVES

To analyze the correlation between ultrasound breast imaging reporting and data system (BI-RADS) category and fluorodeoxyglucose [¹⁸F] (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) findings and their value in breast lesion diagnosis.

MATERIALS AND METHODS

Cases involving hypermetabolic lesions identified by ¹⁸F-FDG PET/CT and ultrasound were retrospectively analyzed. The correlation between the maximum standardized uptake values (SUV_max) of the lesions and the BI-RADS grades was calculated. Histologic diagnosis or evidence at the end of a 2-year follow-up as the standard of truth were analyzed to determine the sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the diagnostic methods. Area under the curve (AUC) of BI-RADS, SUV_max, and BI-RADS/SUV_max combined were obtained using receiver-operating characteristic curve (ROC) analysis.

RESULTS

Of 206 cases, 92 were benign and 114 were malignant. The difference between the SUV_max and the BI-RADS grades was statistically significant (p < 0.001). The critical value of the optimal SUV_max was 2.325, and the accuracy, sensitivity, specificity, PPV, and NPV were 84.5%, 91.2%, 76.1%, 82.5%, and 87.5%, respectively. For diagnosis using BI-RADS, these values were 85.9%, 98.2%, 70.7%, 80.6%, and 97.0%, respectively. ROC analysis of 206 breast lesions for distinguishing benign from malignant lesions yielded AUCs of 0.948, 0.896, and 0.977 for BI-RADS, SUV_max, and BI-RADS/SUV_max combined, respectively. The critical value of the optimal SUV_max in grade 3 and 4 lesions (as determined using BI-RADS) was 2.705, and the accuracy, sensitivity, specificity, PPV, and NPV were 82.6%, 77.8%, 85.7%, 77.8%, and 85.7%, respectively. For diagnosis using BI-RADS in cases with grade 3 and 4 lesions, these values were 68.5%, 94.4%, 51.8%, 55.7%, and 93.5%, respectively. In ROC analysis for distinguishing benign from malignant for BI-RADS grade 3-4 lesions, the AUC of BI-RADS, SUV_max, and BI-RADS/SUV_max combined were 0.731, 0.859, and 0.882, respectively.

CONCLUSION

Both ¹⁸F-FDG PET/CT and ultrasound-dependent BI-RADS grading are effective for diagnosing breast lesions. However, in cases of BI-RADS grades 3 and 4, ¹⁸F-FDG PET/CT has better specificity and may be useful for further differential diagnosis.

Comparison of prone versus supine 18F-FDG-PET of locally advanced breast cancer: Phantom and preliminary clinical studies

PURPOSE

Previous studies have demonstrated how imaging of the breast with patients lying prone using a supportive positioning device markedly facilitates longitudinal and/or multimodal image registration. In this contribution, the authors' primary objective was to determine if there are differences in the standardized uptake value (SUV) derived from [(18)F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in breast tumors imaged in the standard supine position and in the prone position using a specialized positioning device.

METHODS

A custom positioning device was constructed to allow for breast scanning in the prone position. Rigid and nonrigid phantom studies evaluated differences in prone and supine PET. Clinical studies comprised 18F-FDG-PET of 34 patients with locally advanced breast cancer imaged in the prone position (with the custom support) followed by imaging in the supine position (without the support). Mean and maximum values (SUVpeak and SUVmax, respectively) were obtained from tumor regions-of-interest for both positions. Prone and supine SUV were linearly corrected to account for the differences in 18F-FDG uptake time. Correlation, Bland-Altman, and nonparametric analyses were performed on uptake time-corrected and uncorrected data.

RESULTS

SUV from the rigid PET breast phantom imaged in the prone position with the support device was 1.9% lower than without the support device. In the nonrigid PET breast phantom, prone SUV with the support device was 5.0% lower than supine SUV without the support device. In patients, the median (range) difference in uptake time between prone and supine scans was 16.4 min (13.4-30.9 min), which was significantly-but not completely-reduced by the linear correction method. SUVpeak and SUVmax from prone versus supine scans were highly correlated, with concordance correlation coefficients of 0.91 and 0.90, respectively. Prone SUVpeak and SUVmax were significantly lower than supine in both original and uptake time-adjusted data across a range of index times (P < < 0.0001, Wilcoxon signed rank test). Before correcting for uptake time differences, Bland-Altman analyses revealed proportional bias between prone and supine measurements (SUVpeak and SUVmax) that increased with higher levels of FDG uptake. After uptake time correction, this bias was significantly reduced (P < 0.01). Significant prone-supine differences, with regard to the spatial distribution of lesions relative to isocenter, were observed between the two scan positions, but this was poorly correlated with the residual (uptake time-corrected) prone-supine SUVpeak difference (P = 0.78).

CONCLUSIONS

Quantitative 18F-FDG-PET/CT of the breast in the prone position is not deleteriously affected by the support device but yields SUV that is consistently lower than those obtained in the standard supine position. SUV differences between scans arising from FDG uptake time differences can be substantially reduced, but not removed entirely, with the current correction method. SUV from the two scan orientations is quantitatively different and should not be assumed equivalent or interchangeable within the same subject. These findings have clinical relevance in that they underscore the importance of patient positioning while scanning as a clinical variable that must be accounted for with longitudinal PET measurement, for example, in the assessment of treatment response.

Prone Versus Supine Breast FDG-PET/CT for Assessing Locoregional Disease Distribution in Locally Advanced Breast Cancer

RATIONALE AND OBJECTIVES

Prone (18)F fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) may have advantages for breast imaging because of improved separation of deep anatomic structures. There are limited data on whether prone and supine FDG-PET/CT provide similar information regarding breast and axillary disease in the setting of locally advanced breast cancer (LABC). The purpose of this study was to compare the information on locoregional disease distribution provided by prone versus supine FDG-PET in newly diagnosed LABC.

MATERIALS AND METHODS

In an Institutional Review Board-approved prospective trial, 24 patients with newly diagnosed LABC underwent both supine and prone FDG-PET/CT at the same scanning session. Three readers performed an independent review of all scans and categorized the locoregional disease distribution as breast only (BO)-unifocal, BO-multifocal, BO-multicentric, or breast + axillary involvement. For breast + axillary disease, the readers also assessed the number of involved axillary lymph nodes. Interobserver discrepancies were resolved at a consensus reading session.

RESULTS

Two scanning sessions were excluded because the prone scan had omitted part of the axilla from the field of view. In the remaining 22 patients, the consensus categorization of anatomic disease distribution was concordant between prone and supine scanning in 21 patients (linear kappa 0.91, 95% confidence interval [0.79-1]). In the 16 patients with breast + axillary disease, equal numbers of involved lymph nodes were identified on prone and supine scanning in 12 patients, whereas in the remaining four patients, prone scanning resulted in a higher number of visualized lymph nodes.

CONCLUSIONS

Prone and supine FDG-PET/CT provided statistically identical information on locoregional disease distribution in LABC. However, prone scanning may perform better than supine for assessing the number of involved lymph nodes. Prone FDG-PET/CT may be useful in future clinical and research efforts, including hybrid PET-magnetic resonance imaging (MRI) applications.

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.

Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results

PURPOSE

The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level.

METHODS

First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm.

RESULTS

The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)).

CONCLUSIONS

The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.

Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).

Effects of reusing baseline volumes of interest by applying (non-)rigid image registration on positron emission tomography response assessments

Objectives

Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation.

Methods

Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUV_max), average SUV (SUV_mean), peak SUV (SUV_peak), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC).

Results

Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUV_max and SUV_peak,, however, differences were observed for MATV, SUV_mean, TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (<2.8 ml), whereas A50 showed a poorer performance when another area with high uptake was close to the target lesion. All methods were affected by lesions with very heterogeneous tracer uptake.

Conclusions

Non-rigid PET- and CTPET-based image registrations may be used to classify response based on SUV_max and SUV_peak. For other quantitative measures future studies should assess which method is valid for response evaluations by correlating with survival data.

Real time dynamic imaging and current targeted therapies in the war on cancer: a new paradigm

In biology, as every science student is made to learn, ontology recapitulates phylogeny. In medicine, however, oncology recapitulates polemology, the science of warfare: The medical establishment is transitioning from highly toxic poisons that kill rapidly dividing normal and malignant cells with little specificity to tailored therapies that target the tumors with the lethality of the therapeutic warhead. From the advent of the information age with the incorporation of high-tech intelligence, reconnaissance, and surveillance has resulted in "data fusion" where a wide range of information collected in near real-time can be used to redesign most of the treatment strategies currently used in the clinic. The medical community has begun to transition from the 'black box' of tumor therapy based solely on the clinical response to the 'glass box' of dynamic imaging designed to bring transparency to the clinical battlefield during treatment, thereby informing the therapeutic decision to 'retreat or repeat'. The tumor microenvironment is dynamic, constantly changing in response to therapeutic intervention, and therefore the therapeutic assessment must map to this variable and ever-changing landscape with dynamic and non-static imaging capabilities. The path to personalized medicine will require incorporation and integration of dynamic imaging at the bedside into clinical practice for real-time, interactive assessment of response to targeted therapies. The application of advanced real time imaging techniques along with current molecularly targeted anticancer therapies which alter cellular homeostasis and microenvironment can enhance therapeutic interventions in cancer patients and further improve the current status in clinical management of patients with advanced cancers.