Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer

Progress and current utility of radiomics in PET/CT study of non-metastatic breast cancer: A systematic review

AIM

To synthesize the current evidence of the usefulness of radiomics in PET/CT image analysis in local and locally advanced breast cancer. Also, to evaluate the methodological quality of the radiomic studies published.

METHODS

Systematic review of articles in different databases until 2021 using the terms "PET", "radiomics", "texture", "breast". Only articles with human data and that included a PET image were included. Studies with simulated data and with less than 20 patients were excluded. Were extracted sample size, radiotracer used, imaging technique, and radiomics characteristics from each article. The methodological quality of the studies was determined using the QUADAS-2 tool.

RESULTS

18 articles were selected. The retrospective design was the most used. The most studied radiomic characteristic was SUVmax. Several radiomic parameters were correlated with tumor characterization, and tumor heterogeneity proved useful for predicting disease course and response to treatment. Most articles showed a high risk of bias, mainly from the patient selection.

CONCLUSIONS

A high probability of bias was observed in most of the published articles. Radiomics is a developing field and more studies are needed to demonstrate its usefulness in routine clinical practice. The QUADAS-2 tool allows critical assessment of the methodological quality of the available evidence. Despite its limitations, radiomics is shown to be an instrument that can help to achieve personalized oncologic management of breast cancer.

Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer

OBJECTıVES: This study investigates whether textural features (TFs) extracted from ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) are associated with immunohistochemical characteristics (IHCs) of invasive ductal breast carcinoma (IDBC).

MATERIALS AND METHODS

The relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative ¹⁸F-FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37TFs of the primary tumor were extracted from ¹⁸F-FDG PET/CT. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics.

RESULTS

A total of 124 patients were included. Histogram-uniformity, grey-level co-occurrence matrix (GLCM), GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while grey-level run-length matrix (GLRLM), GLRLM-SRHGE, grey-level zone length matrix (GLZLM), GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis.

CONCLUSIONS

ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of ¹⁸F-FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement.

Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics

Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in ¹⁸F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent ¹⁸F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUV_max, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUV_max lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUV_max, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases.

Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer

OBJECTıVES: This study investigates whether textural features (TFs) extracted from F-18 FDG positron emission tomography/computed tomography (PET/CT) are associated with IHCs of invasive ductal breast carcinoma (IDBC).

MATERIALS AND METHODS

The relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative F-18 FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37 TFs of the primary tumor were extracted from F-18 FDG PET/CT. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics.

RESULTS

A total of 124 patients were included. Histogram-uniformity, GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while GLRLM-SRHGE, GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis.

CONCLUSIONS

ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of F-18 FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement.

Application progress of multiple imaging modalities in Takayasu arteritis

Takayasu arteritis (TA) is a chronic, idiopathic, granulomatous large vessel vasculitis of unknown etiology. The clinical manifestations of TA are incredibly variable, mainly depending on the location of the lesions. In the light of its insidious progress and the diversity of clinical manifestations, a substantial proportion of patients might experience a considerable delay in diagnosis, which leads to irreversible malignant complications, highlighting the importance of early diagnosis. There has been accumulating evidence that early identification of disease is pivotal to initiate timely therapy and ameliorate the prognosis. Therefore, this review discusses and summarizes the latest evidence on the application progress of multiple imaging modalities.

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.

Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D

PURPOSE

The aim of our work is to evaluate the correlation of two-dimensional (2D) and three-dimensional (3D) radiomics and metabolic features of hepatocellular carcinoma (HCC) with tumour diameter, staging, and metabolic tumour volume (MTV).

MATERIAL AND METHODS

Thirty-three patients with HCC were studied using 18F-fluorodeoxyglucose positron-emission tomography with computed tomography (18F [FDG] PET/CT). The tumours were segmented from the PET images after CT correction. Metabolic parameters and 35 radiomics features were compared using 2D and 3D modes. The metabolic parameters and tumour morphology were compared using 2 different types of software. Tumour heterogeneity was studied in both metabolic parameters and radiomics features. Finally, the correlation between the metabolic and radiomics features in 3D mode, as well as tumour morphology and staging according to the American Joint Committee on Cancer (AJCC) staging were studied.

RESULTS

Most of the metabolic parameters and radiomics features are statically stable through the 2D and 3D modes. Most of the 3D mode features show a correlation with metabolic parameters; the total lesion glycolysis (TLG) shows the highest correlation, with a Spearman correlation coefficient (rs) of 0.9776. Also, the grey level run length matrix/run length non-uniformity (GLRLM_RLNU) from radiomics features exhibits a correlation with a Spearman correlation coefficient of 0.9733. Maximum tumour diameter is correlated with TLG and GLRLM_RLNU, with rs equal to 0.7461 and 0.7143, respectively. Regarding AJCC staging, some features show a medium but prognostic correlation. In the case of 2D-mode features, all metabolic and radiomics features show no significant correlation with MTV, AJCC staging, and tumour maximum diameter.

CONCLUSIONS

Most of the normal metabolic parameters and radiomics features are statistically stable through the 3D and 2D modes. 3D radiomics features are significantly correlated with tumour volume, maximum diameter, and staging. Conversely, 2D features have negligible correlation with the same parameters. Therefore, 3D mode features are preferable and can accurately evaluate tumour heterogeneity.

Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy

OBJECTIVES

To explore the prognostic value of positron emission tomography (PET) radiomic features in the field of diffuse large B cell lymphoma (DLBCL) treated with a first-line immunochemotherapy.

METHODS

One-hundred thirty-two patients newly diagnosed with DLBCL were retrospectively included. PET studies were reconstructed using an ordered subset expectation maximisation algorithm with point spread function modelling. The total metabolic tumour volume (MTV) was recorded for each patient, and the volume of interest structure of the largest target lesion was used to compute ¹⁸F-FDG textural parameters. Data was randomly split into training and validation datasets. Optimal cutoff values were determined by means of 2-year event-free survival (EFS) ROC analyses. Two-year EFS analyses were performed using Kaplan-Meier survival analyses and univariable and multivariable Cox regression models.

RESULTS

The median follow-up was 27 months, and the 2-year event-free survival (2y-EFS) was 77.3% in the entire population. ROC analyses for the 2y-EFS reached statistical significance for total MTV as well as four second-order metrics (homogeneity, contrast, correlation, dissimilarity) and five third-order metrics (LZE (Long-Zone Emphasis), LZLGE (Long-Zone Low-Grey Level Emphasis), LZHGE (Long-Zone High-Grey Level Emphasis), GLNU (Grey-Level Non-Uniformity) and ZP (Zone Percentage)). LZHGE displayed the highest ROC analysis accuracy (acc. = 0.76) and the best discriminant value on univariable Kaplan-Meier analysis (p < 0.0001, HR = 4.54). On multivariable analysis, including IPIaa, total MTV and LZHGE, LZHGE was the only independent predictor of 2y-EFS. These results were confirmed on the validation dataset.

CONCLUSIONS

Baseline ¹⁸F-FDG PET heterogeneity of the largest lymphoma lesion is a promising predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy.

KEY POINTS

•¹⁸F-FDG metabolic heterogeneity emerges as a new tool for survival prognostication of patients and has been explored in many solid tumours with promising results. • Baseline¹⁸F-FDG PET heterogeneity of the largest lymphoma lesion is an independent predictor of 2y-EFS in newly diagnosed DLBCL treated with immunochemotherapy. • DLBCL patients presenting with a heterogeneous tumour displayed a worse prognosis.

PET/CT radiomics in breast cancer: Mind the step

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.

Segmentation of gliomas in 18F-fluorocholine PET/CT. A multiapproach study

AIM

Our aim was two-fold, to study the interobserver agreement in tumour segmentation and to search for a reliable methodology to segment gliomas using ¹⁸F-fluorocholine PET/CT.

METHODS

25 patients with glioma, from a prospective and non-randomized study (Functional and Metabolic Glioma Analysis), were included.Interobserver variability in tumour segmentation was assessed using fixed thresholds. Different strategies were used to segment the tumours. First, a semi-automatic tumour segmentation was performed, selecting the best SUVmax-% threshold for each lesion. Next we determined a variable SUVmax-% depending on the SUVmax. Finally a segmentation using a fixed SUVmax threshold was performed. To do so, a sampling of 10 regions of interest (ROI of 2.8cm²) located in the normal brain was performed. The upper value of the sample mean SUVmax±3 SD was used as cut-off. All procedures were tested and classified as effective or not for tumour segmentation by two observer's consensus.

RESULTS

In the pilot segmentation, the mean±SD of SUVmax, SUVmean and optimal SUVmax-% threshold were: 3.64±1.77, 1.32±0.57 and 21.32±8.39, respectively. Optimal SUVmax-% threshold showed a significant association with the SUVmax (Pearson=-0.653, p=.002). However, the linear regression model for the total sample was not good, that supported the division in two homogeneous groups, defining two formulas for predicting the optimal SUVmax-% threshold. As to the third procedure, the obtained value for the mean SUVmax background+3 SD was 0.33. This value allowed segmenting correctly a significant fraction of tumours, although not all.

CONCLUSION

A great interobserver variability in the tumour segmentation was found. None of the methods was able to segment correctly all the gliomas, probably explained by the wide tumour heterogeneity on ¹⁸F-fluorocholine PET/CT.

Global heterogeneity assessed with 18F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer

AIM

To analyze the relationship between measurements of global heterogeneity, obtained from ¹⁸F-FDG PET/CT, with biological variables, and their predictive and prognostic role in patients with locally advanced breast cancer (LABC).

MATERIAL AND METHODS

68 patients from a multicenter and prospective study, with LABC and a baseline ¹⁸F-FDG PET/CT were included. Immunohistochemical profile [estrogen receptors (ER) and progesterone receptors (PR), expression of the HER-2 oncogene, Ki-67 proliferation index and tumor histological grade], response to neoadjuvant chemotherapy (NC), overall survival (OS) and disease-free survival (DFS) were obtained as clinical variables. Three-dimensional segmentation of the lesions, providing SUV, volumetric [metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] and global heterogeneity variables [coefficient of variation (COV) and SUVmean/SUVmax ratio], as well as sphericity was performed. The correlation between the results obtained with the immunohistochemical profile, the response to NC and survival was also analyzed.

RESULTS

Of the patients included, 62 received NC. Only 18 responded. 13 patients relapsed and 11 died during follow-up. ER negative tumors had a lower COV (p=0.018) as well as those with high Ki-67 (p=0.001) and high risk phenotype (p=0.033) compared to the rest. No PET variable showed association with the response to NC nor OS. There was an inverse relationship between sphericity with DFS (p=0.041), so, for every tenth that sphericity increases, the risk of recurrence decreases by 37%.

CONCLUSIONS

Breast tumors in our LABC dataset behaved as homogeneous and spherical lesions. Larger volumes were associated with a lower sphericity. Global heterogeneity variables and sphericity do not seem to have a predictive role in response to NC nor in OS. More spherical tumors with less variation in gray intensity between voxels showed a lower risk of recurrence.

Prognostic models based on imaging findings in glioblastoma: Human versus Machine

Many studies have built machine-learning (ML)-based prognostic models for glioblastoma (GBM) based on radiological features. We wished to compare the predictive performance of these methods to human knowledge-based approaches. 404 GBM patients were included (311 discovery and 93 validation). 16 morphological and 28 textural descriptors were obtained from pretreatment volumetric postcontrast T1-weighted magnetic resonance images. Different prognostic ML methods were developed. An optimized linear prognostic model (OLPM) was also built using the four significant non-correlated parameters with individual prognosis value. OLPM achieved high prognostic value (validation c-index = 0.817) and outperformed ML models based on either the same parameter set or on the full set of 44 attributes considered. Neural networks with cross-validation-optimized attribute selection achieved comparable results (validation c-index = 0.825). ML models using only the four outstanding parameters obtained better results than their counterparts based on all the attributes, which presented overfitting. In conclusion, OLPM and ML methods studied here provided the most accurate survival predictors for glioblastoma to date, due to a combination of the strength of the methodology, the quality and volume of the data used and the careful attribute selection. The ML methods studied suffered overfitting and lost prognostic value when the number of parameters was increased.

Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics

BACKGROUND

The aim of this study is to determine if the choice of the ¹⁸F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs).

PROCEDURES

Forty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSF_WholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEM_breast/PSF_breast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics.

RESULTS

PSF_WholeBody showed lower numbers of voxels within VOIs than OSEM_breast and PSF_breast with median (interquartile range) equal to 130 (43-271), 316 (167-1042), 367 (107-1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSF_WholeBody, OSEM_breast and PSF_breast, respectively. On matched comparisons, PSF_breast reconstruction presented better abilities than PSF_wholeBody and OSEM_breast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSF_wholeBody and 73.8% for OSEM_breast. PSF_breast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUV_max on PSF_wholeBody images (ρ = - 0.526, p = 0.005), whereas it was not on OSEM_breast (ρ = - 0.183, p = 0.244) and PSF_breast (ρ = - 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSF_breast images as compared to OSEM_breast, especially in small lesions (MTV < 3 ml).

CONCLUSIONS

High-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.