Texture analysis of high-resolution dedicated breast 18 F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer

Clinical Applications of Dedicated Breast Positron Emission Tomography

Breast-specific positron imaging systems provide higher sensitivity than whole-body PET for breast cancer detection. The clinical applications for breast-specific positron imaging are similar to breast MRI including preoperative local staging and neoadjuvant therapy response assessment. Breast-specific positron imaging may be an alternative for patients who cannot undergo breast MRI. Further research is needed in expanding the field-of-view for posterior breast lesions, increasing biopsy capability, and reducing radiation dose. Efforts are also necessary for developing appropriate use criteria, increasing availability, and advancing insurance coverage.

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy.

RESULTS

This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938).

CONCLUSION

The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

Predictive value of radiomic signature based on 2-[18F]FDG PET/CT in HER2 status determination for primary breast cancer with equivocal IHC results

PURPOSE

To evaluate the predictive power of 2-[18F]FDG PET/CT-derived radiomic signature in human epidermal growth factor receptor 2 (HER2) status determination for primary breast cancer (BC) with equivocal immunohistochemistry (IHC) results for HER2.

METHODS

A total of 154 primary BC with equivocal IHC results for HER2 were retrospectively enrolled in the study. First, the following five conventional PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were measured and compared between HER2-positive and HER2-negative cohorts. After quantitative radiomic features extraction and reduction, the least absolute shrinkage and selection operator (LASSO) algorithm was used to establish a radiomic signature model. Then, the area under the curve (AUCs) after a receiver operator characteristic (ROC) analysis, accuracy, sensitivity and specificity were calculated and used as the main outcomes. Finally, a total of 37 BC patients from an external institution were included to perform an external validation.

RESULTS

All the five conventional PET parameters were unable to discriminate between HER2-positive and HER2-negative cohorts for BC (P = 0.104-0.544). Whereas, the developed radiomic signature model was potentially predictive of HER2 status with an of AUC 0.887 (95% confidence interval [CI], 0.824-0.950) in the training cohort and 0.766 (95% CI, 0.616-0.916) in the validation cohort, respectively. For external validation, the AUC for the external test cohort was 0.788 (95% CI, 0.633-0.944).

CONCLUSIONS

Radiomic signature based on 2-[18F]FDG PET/CT images was capable of non-invasively predicting the HER2 status with a comparable ability to FISH assay, especially for those with equivocal IHC results for HER2.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

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.

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature

Objective

To illustrate the knowledge hotspots and cutting-edge research trends of ¹⁸F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed.

Methods

Studies related to ¹⁸F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of ¹⁸F-FDG PET/CT radiomics in this field utilizes a citation burst detection method.

Results

Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. ¹⁸F-FDG PET/CT radiomics–related literature was frequently published with high citation in European Journal of Nuclear Medicine and Molecular Imaging (IF9.236, 2020), Frontiers in Oncology (IF6.244, 2020), and Cancers (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in ¹⁸F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of ¹⁸F-FDG PET/CT radiomics features.

Conclusion

First, this bibliometric study provides a new perspective on ¹⁸F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of ¹⁸F-FDG PET/CT. Second, the international research frontiers lie in applying ¹⁸F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of ¹⁸F-FDG PET/CT radiomics features.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes

PURPOSE

This study aimed to quantitatively assess [¹⁸F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [¹⁸F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC.

METHODS

The 60-min dynamic total-body [¹⁸F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry.

RESULTS

A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV_mean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUV_mean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3⁺/CD8⁺) and macrophages (CD68⁺/CD163⁺) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups.

CONCLUSION

The dynamic total-body [¹⁸F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUV_mean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [¹⁸F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [18F]FDG PET/CT in Early Triple-Negative Breast Cancer

Simple Summary

The aim of this study was to evaluate PET/CT parameters to determine different prognostic groups in TNBC, in order to select patients with a high risk of relapse, for whom therapeutic escalation can be considered. We have demonstrated that the MTV, TLG and entropy of the primary breast lesion could be of interest to predict the prognostic outcome of TNBC patients.

Abstract

(1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [¹⁸F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [¹⁸F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [¹⁸F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUV_max, SUV_mean, SUV_peak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [¹⁸F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.

Single-Photon Emission Computed Tomography/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors

Purpose: The purpose of this study was to investigate the feasibility of Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) image-based radiomics in differentiating bone metastases from benign bone lesions in patients with tumors. Methods: A total of 192 lesions from 132 patients (134 in the training group, 58 in the validation group) diagnosed with vertebral bone metastases or benign bone lesions were enrolled. All images were evaluated and diagnosed independently by two physicians with more than 20 years of diagnostic experience for qualitative classification, the images were imported into MaZda software in Bitmap (BMP) format for feature extraction. All radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation algorithms after the process of normalization and correlation analysis. Based on these selected features, two models were established: The CT model and SPECT model (radiomics features were derived from CT and SPECT images, respectively). In addition, a combination model (ComModel) combined CT and SPECT features was developed in order to better evaluate the predictive performance of radiomics models. Subsequently, the diagnostic performance between each model was separately evaluated by a confusion matrix. Results: There were 12, 13, and 18 features contained within the CT, SPECT, and ComModel, respectively. The constructed radiomics models based on SPECT/CT images to discriminate between bone metastases and benign bone lesions not only had high diagnostic efficacy in the training group (AUC of 0.894, 0.914, 0.951 for CT model, SPECT model, and ComModel, respectively), but also performed well in the validation group (AUC; 0.844, 0.871, 0.926). The AUC value of the human experts was 0.849 and 0.839 in the training and validation groups, respectively. Furthermore, both SPECT model and ComModel show higher classification performance than human experts in the training group (P = 0.021 and P = 0.001, respectively) and the validation group (P = 0.037 and P = 0.007, respectively). All models showed better diagnostic accuracy than human experts in the training group and the validation group. Conclusion: Radiomics derived from SPECT/CT images could effectively discriminate between bone metastases and benign bone lesions. This technique may be a new non-invasive way to help prevent unnecessary delays in diagnosis and a potential contribution in disease staging and treatment planning.

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.

MRI-aided kernel PET image reconstruction method based on texture features

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.

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.

Radiomics: A Primer for Breast Radiologists

Radiomics has a long-standing history in breast imaging with computer-aided detection (CAD) for screening mammography developed in the late 20th century. Although conventional CAD had widespread adoption, the clinical benefits for experienced breast radiologists were debatable due to high false-positive marks and subsequent increased recall rates. The dramatic growth in recent years of artificial intelligence-based analysis, including machine learning and deep learning, has provided numerous opportunities for improved modern radiomics work in breast imaging. There has been extensive radiomics work in mammography, digital breast tomosynthesis, MRI, ultrasound, PET-CT, and combined multimodality imaging. Specific radiomics outcomes of interest have been diverse, including CAD, prediction of response to neoadjuvant therapy, lesion classification, and survival, among other outcomes. Additionally, the radiogenomics subfield that correlates radiomics features with genetics has been very proliferative, in parallel with the clinical validation of breast cancer molecular subtypes and gene expression assays. Despite the promise of radiomics, there are important challenges related to image normalization, limited large unbiased data sets, and lack of external validation. Much of the radiomics work to date has been exploratory using single-institution retrospective series for analysis, but several promising lines of investigation have made the leap to clinical practice with commercially available products. As a result, breast radiologists will increasingly be incorporating radiomics-based tools into their daily practice in the near future. Therefore, breast radiologists must have a broad understanding of the scope, applications, and limitations of radiomics work.

Application-specific nuclear medicalin vivoimaging devices

Nuclear medical imaging devices, such as those enabling photon emission imaging (gamma camera, single photon emission computed tomography, or positron emission imaging), that are typically used in today's clinics are optimized for assessing large portions of the human body, and are classified as whole-body imaging systems. These systems have known limitations for organ imaging, therefore application-specific devices have been designed, constructed and evaluated. These devices, given their compact nature and superior technical characteristics, such as their higher detection sensitivity and spatial resolution for organ imaging compared to whole-body imaging systems, have shown promise for niche applications. Several of these devices have further been integrated with complementary anatomical imaging devices. The objectives of this review article are to (1) provide an overview of such application-specific nuclear imaging devices that were developed over the past two decades (in the twenty-first century), with emphasis on brain, cardiac, breast, and prostate imaging; and (2) discuss the rationale, advantages and challenges associated with the translation of these devices for routine clinical imaging. Finally, a perspective on the future prospects for application-specific devices is provided, which is that sustained effort is required both to overcome design limitations which impact their utility (where these exist) and to collect the data required to define their clinical value.

Breast cancer detection by dedicated breast positron emission tomography according to the World Health Organization classification of breast tumors

OBJECTIVE

Considering the difficulty in detecting primary breast cancers using whole-body positron emission tomography (WBPET) owing to its limited spatial resolution, we aimed to evaluate the detectability of breast cancer by ring-type dedicated breast PET (DbPET) on the World Health Organization (WHO) histological classification in comparison with WBPET.

METHODS

A total of 938 patients with breast cancer underwent WBPET and ring-type DbPET, and 1021 lesions were histologically assessed based on the WHO classification of tumors of the breast. The findings of WBPET and DbPET were retrospectively evaluated and compared.

RESULTS

The size-related sensitivity of DbPET was superior to that of WBPET for subcentimetric tumors (81.9% vs. 52.4%, P < 0.001). The histological distribution was as follows: 11 lobular carcinoma in situ, 158 ductal carcinoma in situ, 738 infiltrating duct carcinoma not otherwise specified (NOS), 12 lobular carcinoma NOS, 40 mucinous adenocarcinoma, 13 tubular carcinoma, 36 invasive breast carcinoma others, and 13 papillary neoplasms. WBPET had low sensitivity for lobular carcinoma in situ, ductal carcinoma in situ, lobular carcinoma NOS, mucinous adenocarcinoma, and tubular carcinoma. DbPET showed improved sensitivity for all the above except lobular and tubular carcinoma. The maximum standardized uptake values (SUVmax) of DbPET were significantly higher than those of WBPET for histological types, excluding lobular carcinoma in situ. The SUVmax of papillary neoplasms was high regardless of low-grade histology and Ki-67 labeling index.

CONCLUSIONS

DBPET was found to have high sensitivity and SUVmax values for all histologic types that showed low sensitivity of detection on WBPET, except lobular carcinoma in situ.

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.

Performance of dedicated breast positron emission tomography in the detection of small and low-grade breast cancer

PURPOSE

This study compares the sensitivity of dedicated breast positron emission tomography (DbPET) and whole body positron emission tomography (WBPET) in detecting invasive breast cancer based on tumor size and biology. Further, we explored the relationship between maximum standardized uptake value (SUVmax) of DbPET and biological features of the tumor.

METHODS

A total of 639 invasive breast cancer lesions subjected to both DbPET and WBPET before surgery, between January 2016 and May 2019, were included in the study. The sensitivity of DbPET and WBPET in detection and the biology of the tumor according to the clinicopathological features were retrospectively evaluated.

RESULTS

The overall sensitivity of DbPET was higher than that of WBPET (91.4% vs. 80.3%, p < 0.001). Subcentimetric tumors were significant (80.9% vs. 54.3%, p < 0.001). Regardless of the nuclear grade, DbPET could detect more lesions than WBPET. The SUVmax was positively correlated with tumor size (R = 0.395, p < 0.001) and the nuclear grade (p < 0.001). Luminal A-like breast cancer had significantly lower SUVmax values than the other subtypes (p < 0.001).

CONCLUSIONS

DbPET is superior to WBPET in the detection of subcentimetric, low-grade breast cancers. Further, by using SUVmax, DbPET can distinguish luminal A-like breast cancer from the other subtypes.

Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

Objective: This retrospective study aimed to compare the ability to classify tumor characteristics of breast cancer (BC) of positron emission tomography (PET)-derived texture features between dedicated breast PET (dbPET) and whole-body PET/computed tomography (CT).

Methods: Forty-four BCs scanned by both high-resolution ring-shaped dbPET and whole-body PET/CT were analyzed. The primary BC was extracted with a standardized uptake value (SUV) threshold segmentation method. On both dbPET and PET/CT images, 38 texture features were computed; their ability to classify tumor characteristics such as tumor (T)-category, lymph node (N)-category, molecular subtype, and Ki67 levels was compared. The texture features were evaluated using univariate and multivariate analyses following principal component analysis (PCA). AUC values were used to evaluate the diagnostic power of the computed texture features to classify BC characteristics.

Results: Some texture features of dbPET and PET/CT were different between Tis-1 and T2-4 and between Luminal A and other groups, respectively. No association with texture features was found in the N-category or Ki67 level. In contrast, receiver-operating characteristic analysis using texture features' principal components showed that the AUC for classification of any BC characteristics were equally good for both dbPET and whole-body PET/CT.

Conclusions: PET-based texture analysis of dbPET and whole-body PET/CT may have equally good classification power for BC.

Evaluation of primary breast cancers using dedicated breast PET and whole-body PET

Metabolic imaging of the primary breast tumor with ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) PET may assist in predicting treatment response in the neoadjuvant chemotherapy (NAC) setting. Dedicated breast PET (dbPET) is a high-resolution imaging modality with demonstrated ability in highlighting intratumoral heterogeneity and identifying small lesions in the breast volume. In this study, we characterized similarities and differences in the uptake of [¹⁸F]FDG in dbPET compared to whole-body PET (wbPET) in a cohort of ten patients with biopsy-confirmed, locally advanced breast cancer at the pre-treatment timepoint. Patients received bilateral dbPET and wbPET following administration of 186 MBq and 307 MBq [¹⁸F]FDG on separate days, respectively. [¹⁸F]FDG uptake measurements and 20 radiomic features based on morphology, tumor intensity, and texture were calculated and compared. There was a fivefold increase in SUL_peak for dbPET (median difference (95% CI): 4.0 mL⁻¹ (1.8–6.4 mL⁻¹), p = 0.006). Additionally, spatial heterogeneity features showed statistically significant differences between dbPET and wbPET. The higher [¹⁸F]FDG uptake in dbPET highlighted the dynamic range of this breast-specific imaging modality. Combining with the higher spatial resolution, dbPET may be able to detect treatment response in the primary tumor during NAC, and future studies with larger cohorts are warranted.

Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

OBJECTIVES

We aimed at investigating the origin of the correlations between tumor volume and ¹⁸F-FDG-PET texture indices in lung cancer.

METHODS

Eighty-five consecutive patients with newly diagnosed non-small cell lung cancer (NSCLC) underwent a ¹⁸F-FDG-PET/CT scan before treatment. Seven phantom spheres uniformly filled with ¹⁸F-FDG, and covering a range of activities and volumes similar to that found in lung tumors, were also scanned. Established texture indices were computed for lung tumors and homogeneous spheres. The dependence between textural indices and volume in homogeneous spheres was modeled and then used to predict texture indices in lung tumors. Correlation analyses were carried out between predicted and texture features measured in lung tumors. Cox proportional hazards regression was used to investigate the associations between overall survival and volume-adjusted textural features.

RESULTS

All textural features showed strong, non-linear correlations with volume, both in tumors and homogeneous spheres. Correlations between predicted versus measured texture features were very high for contrast (r² = 0.91), dissimilarity (r² = 0.90), ZP (r² = 0.90), GLNN (r² = 0.86), and homogeneity (r² = 0.82); high for entropy (r² = 0.50) and HILAE (r² = 0.53); and low for energy (r² = 0.30). Cox regressions showed that among volume-adjusted features, only HILAE was associated with overall survival (b = - 0.35, p = 0.008).

CONCLUSION

We have shown that texture indices previously found to be correlated with a number of clinically relevant outcomes might not provide independent information apart from that driven by their correlation with tumor volume, suggesting that these metrics might not be suitable as intratumor heterogeneity markers.

KEY POINTS

• Associations between texture FDG-PET indices and overall survival have been widely reported in lung cancer, with tumor volume also being associated with overall survival, and therefore, it is still unclear whether the predictive power of textural indices is simply driven by this correlation. • Our results demonstrated strong non-linear correlations between textural indices and volume, showing an analogous behavior for lung tumors from patients and homogeneous spheres inserted in phantoms. • Our findings showed that texture FDG-PET indices might not provide independent information apart from that driven by their correlation with tumor volume.

Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer

Breast cancer is a major disease with high morbidity and mortality in women worldwide. Increased use of imaging biomarkers has been shown to add more information with clinical utility in the detection and evaluation of breast cancer. To date, numerous studies related to PET-based imaging in breast cancer have been published. Here, we review available studies on the clinical utility of different PET-based molecular imaging methods in breast cancer diagnosis, staging, distant-metastasis detection, therapeutic and prognostic prediction, and evaluation of therapeutic responses. For primary breast cancer, PET/MRI performed similarly to MRI but better than PET/CT. PET/CT and PET/MRI both have higher sensitivity than MRI in the detection of axillary and extra-axillary nodal metastases. For distant metastases, PET/CT has better performance in the detection of lung metastasis, while PET/MRI performs better in the liver and bone. Additionally, PET/CT is superior in terms of monitoring local recurrence. The progress in novel radiotracers and PET radiomics presents opportunities to reclassify tumors by combining their fine anatomical features with molecular characteristics and develop a beneficial pathway from bench to bedside to predict the treatment response and prognosis of breast cancer. However, further investigation is still needed before application of these modalities in clinical practice. In conclusion, PET-based imaging is not suitable for early-stage breast cancer, but it adds value in identifying regional nodal disease and distant metastases as an adjuvant to standard diagnostic imaging. Recent advances in imaging techniques would further widen the comprehensive and convergent applications of PET approaches in the clinical management of breast cancer.

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.

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

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.

Tumor Heterogeneity on FDG PET/CT and Immunotherapy: An Imaging Biomarker for Predicting Treatment Response in Patients With Metastatic Melanoma

OBJECTIVE. The purpose of this study is to evaluate the ability of quantitative ¹⁸F-FDG PET parameters to predict outcomes of patients with malignant melanoma who have been treated with immune modulation therapy. MATERIALS AND METHODS. We retrospectively investigated 34 patients with malignant melanoma. Twenty-three patients received immunotherapy as first-line therapy, and 11 patients received it as second-line therapy. The maximum standardized uptake value (SUV_max), metabolic tumor volume, tumor lesion glycolysis, and intratumoral metabolic heterogeneity (as measured by the tumor heterogeneity [TH] index) were measured for the primary tumors and metastatic sites associated with up to five of the most FDG-avid lesions per patient. The TH index was calculated as the AUC value of a cumulative SUV volume histogram curve for all patients. The median follow-up was 29.5 months (range, 3-288 months). Outcome endpoints were progression-free survival and overall survival. Kaplan-Meier survival plots were used, and Cox regression analysis was performed for predictors of survival. RESULTS. A total of 101 lesions were analyzed. Five lesions were analyzed in 12 patients, four lesions in three patients, three lesions in three patients, two lesions in four patients, and one lesion in 12 patients. Of the 34 patients included in the study, 15 (44.1%) had disease progression and 11 (32.3%) had died by the time the last follow-up occurred. The mean (± SD) SUV_max, peak SUV, metabolic tumor volume, tumor lesion glycolysis, and TH values for all lesions were 9.68 ± 6.6, 7.82 ± 5.83, 81.96 ± 146.87 mL, 543.65 ± 1022.92 g, and 5841.36 ± 1249.85, respectively. TH had a negative correlation with SUV_max, peak SUV, and tumor lesion glycolysis (p < 0.0001 for all). CONCLUSION. The TH index is significantly associated with overall survival in patients with metastatic melanoma treated with immune modulation therapy as first-line or second-line therapy.

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.

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 ¹⁸F-labeled 2-deoxy-2fluoro-d-glucose ([¹⁸F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P  <  0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P  <  0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78-1.00), colour features showed an AUC of 0.85 (95% CI, 0.71-0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48-0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P  =  0.010), but not significantly different from that for colour features only (P  =  0.328). HSV colour features showed a similar performance to RGB colour features (P  =  0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance.