[18F]FDG PET/CT features for the molecular characterization of primary breast tumors

Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer?

INTRODUCTION

Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results.

AREAS COVERED

This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics.

EXPERT OPINION

Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PURPOSE

To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC).

METHODS

From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated.

RESULTS

The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722.

CONCLUSIONS

Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Overall survival and short-term efficacy analysis of cervical squamous cell carcinoma with skeletal muscle and 18F-FDG PET/CT parameters

2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) can provide tumor biological metabolism and skeletal muscle composition information. The aim of this study was to evaluate overall survival (OS) and short-term efficacy of cervical squamous cell carcinoma combining tumor biological metabolism and skeletal muscle composition parameters. Eighty two patients with cervical squamous cell carcinoma were included in the study, who received 18F-FDG PET/CT scans before treatment. Clinical characteristics, tumor biological metabolism parameters [standardized uptake value, metabolic tumor volume (MTV), total lesion glycolysis, heterogeneity of tumors, etc.] and body composition parameters were recorded. The survival analysis of cervical squamous cell carcinoma patients was performed by univariate and multivariate analysis. A combined model included clinical indicators, tumor metabolism parameters and sarcopenia was constructed to evaluate OS of patients. According to the Response Evaluation Criteria in Solid Tumours version 1.1, the relationship between sarcopenia with tumor metabolism parameters and short-term efficacy was investigated in subgroup. The results indicate that sarcopenia and high value of the sum of MTV of lesions and metastases (MTVtotal) were poor prognostic factors in patients with cervical squamous cell carcinoma. The combination of sarcopenia, MTVtotal and clinical factors provided an improved prediction of OS especially in the long term after treatment. Nutritional status of the patients and tumor metabolism may not affect the short-term efficacy of chemoradiotherapy in cervical squamous cell carcinoma patients.

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.

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

OBJECTIVE

In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way.

MATERIALS AND METHODS

A total of 48 BC patients, who were initially diagnosed by ¹⁸F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model.

RESULTS

In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_Sphericity_CT (CT Sphericity from SHAPE), and GLCM_Contrast_CT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_Contrast_CT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05.

CONCLUSIONS

A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

18F-FDG PET/CT radiomics signature and clinical parameters predict progression-free survival in breast cancer patients: A preliminary study

Introduction

This study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.

Methods

Breast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecularsubtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.

Results

Eleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P&lt; 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P&lt; 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726–0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.

Conclusion

The ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.

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.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Computed Tomography Radiomics Model Predicts Procedure Success of Coronary Chronic Total Occlusions

BACKGROUND

Coronary computed tomography (CT) angiography imaging is useful for the preprocedural evaluation of chronic total occlusion (CTO). However, the predictive value of CT radiomics model for successful percutaneous coronary intervention (PCI) has not been studied. We aimed to develop and validate a CT radiomics model for predicting PCI success of CTOs.

METHODS

In this retrospective study, a radiomics-based model for predicting PCI success was developed on the training and internal validation sets of 202 and 98 patients with CTO, collected from 1 tertiary hospital. The proposed model was validated on an external test set of 75 CTO patients enrolled from another tertiary hospital. CT radiomics features of each CTO lesion were manually labeled and extracted. Other anatomical parameters, including occlusion length, entry morphology, tortuosity, and calcification burden, were also measured. Fifteen radiomics features, 2 quantitative plaque features, and CT-derived Multicenter CTO Registry of Japan score were used to train different models. The predictive values of each model were evaluated for predicting revascularization success.

RESULTS

In the external test set, 75 patients (60 men; 65 years [58.5, 71.5]) with 83 CTO lesions were assessed. Occlusion length was shorter (13.00 mm versus 29.30 mm, P=0.007) in PCI success group whereas the presence of tortuous course was more commonly presented in PCI failure group (1.49% versus 25.00%, P=0.004). The radiomics score was significantly smaller in PCI success group (0.10 versus 0.55, P<0.001). The area under the curve of CT radiomics-based model was significantly higher than that of CT-derived Multicenter CTO Registry of Japan score for predicting PCI success (area under the curve=0.920 versus 0.752, P=0.008). The proposed radiomics model accurately identified 89.16% (74/83) CTO lesions with procedure success.

CONCLUSIONS

CT radiomics-based model outperformed CT-derived Multicenter CTO Registry of Japan score for predicting PCI success. The proposed model is more accurate than the conventional anatomical parameters to identify CTO lesions with PCI success.

Assessment and Response to Neoadjuvant Treatments in Breast Cancer: Current Practice, Response Monitoring, Future Approaches and Perspectives

Neoadjuvant treatments (NAT) for breast cancer (BC) consist in the administration of chemotherapy-more rarely endocrine therapy-before surgery. Firstly, it was introduced 50 years ago to downsize locally advanced (inoperable) BCs. NAT are now widespread and so effective to be used also at the early stage of the disease. NAT are heterogeneous in terms of therapeutic patterns, class of used drugs, dosage, and duration. The poly-chemotherapy regimen and administration schedule are established by a multi-disciplinary team, according to the stage of disease, the tumor subtype and the age, the physical status, and the drug sensitivity of BC patients. Consequently, an accurate monitoring of treatment response can provide significant clinical advantages, such as the treatment de-escalation in case of early recognition of complete response or, on the contrary, the switch to an alternative treatment path in case of early detection of resistance to the ongoing therapy. Future is going toward increasingly personalized therapies and the prediction of individual response to treatment is the key to practice customized care pathways, preserving oncological safety and effectiveness. To gain such goal, the development of an accurate monitoring system, reproducible and reliable alone or as part of more complex diagnostic algorithms, will be promising.

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.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer

Background

This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis.

Methods

This retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N0 (negative ALN metastasis), N1–2 (low metastatic burden with 1–2 ALNs), and N≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N0, N1–2, and N≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined.

Results

There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N0, N1–2, and N≥ 3 groups (P < 0.05). The energy differed significantly between N0 and N1–2 groups (P < 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N0 and N≥3, or N1–2 and N≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N0 and N1–2 groups from N≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982.

Conclusion

By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer.

Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival

Purpose: We aim to develop and validate PET/ CT image-based radiomics to determine the Ki-67 status of high-grade serous ovarian cancer (HGSOC), in which we use the metabolic subregion evolution to improve the prediction ability of the model. At the same time, the stratified effect of the radiomics model on the progression-free survival rate of ovarian cancer patients was illustrated.

Materials and methods: We retrospectively reviewed 161 patients with HGSOC from April 2013 to January 2019. 18F-FDG PET/ CT images before treatment, pathological reports, and follow-up data were analyzed. A randomized grouping method was used to divide ovarian cancer patients into a training group and validation group. PET/ CT images were fused to extract radiomics features of the whole tumor region and radiomics features based on the Habitat method. The feature is dimensionality reduced, and meaningful features are screened to form a signature for predicting the Ki-67 status of ovarian cancer. Meanwhile, survival analysis was conducted to explore the hierarchical guidance significance of radiomics in the prognosis of patients with ovarian cancer.

Results: Compared with texture features extracted from the whole tumor, the texture features generated by the Habitat method can better predict the Ki-67 state (p &lt; 0.001). Radiomics based on Habitat can predict the Ki-67 expression accurately and has the potential to become a new marker instead of Ki-67. At the same time, the Habitat model can better stratify the prognosis (p &lt; 0.05).

Conclusion: We found a noninvasive imaging predictor that could guide the stratification of prognosis in ovarian cancer patients, which is related to the expression of Ki-67 in tumor tissues. This method is of great significance for the diagnosis and treatment of ovarian cancer.

Combined 18F-FDG and 18F-Alfatide II PET May Predict Luminal B (HER2 Negative) Subtype and Nonluminal Subtype of Invasive Breast Cancer

Noninvasive PET molecular imaging using radiopharmaceuticals is important to classify breast cancer in the clinic. The aim of this study was to investigate the combination of ¹⁸F-FDG and ¹⁸F-Alfatide II for predicting molecular subtypes of invasive breast cancer. Forty-four female patients with clinically suspected breast cancer were recruited and underwent ¹⁸F-FDG and ¹⁸F-Alfatide II PET/CT within a week. Tracer uptake in breast lesions was assessed using the maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), and SUV_max ratio of ¹⁸F-FDG to ¹⁸F-Alfatide II (FAR). Invasive breast cancer lesions were further classified as luminal A subtype, luminal B subtype, human epidermal growth factor receptor-2 (HER2) overexpressing subtype, and triple negative subtype according to the expression of the estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67. Among 44 patients, 35 patients were pathologically diagnosed with invasive breast cancer. The SUV_max and SUV_mean of ¹⁸F-FDG were significantly higher in the ER-negative group than those in the ER-positive group, as well as in the PR-negative group than those in the PR-positive group. However, the SUV_max and SUV_mean of ¹⁸F-Alfatide II were higher in the ER-positive group and the PR-positive group. By combining ¹⁸F-FDG and ¹⁸F-Alfatide II, the FAR was lower in the ER-positive group and the PR-positive group. The HER2 overexpressing subtype showed the highest SUV_max and SUV_mean for ¹⁸F-FDG while the luminal B (HER2 negative) subtype revealed the lowest values. The luminal B (HER2 negative) subtype showed the highest ¹⁸F-Alfatide II SUV_max, while the triple negative subtype showed the lowest ¹⁸F-Alfatide II SUV_max. The FAR was the lowest in the luminal B (HER2 negative) subtype and much higher in the HER2 overexpressing and triple negative subtypes. FAR less than 1 predicted the luminal B (HER2 negative) subtype with high specificity (93.1%) and NPV (90%). FAR greater than 3 predicted the HER2 overexpressing subtype and triple negative subtype (namely, the nonluminal subtype) with very high specificity (100%) and PPV (100%). In summary, FAR, the combined PET parameter of ¹⁸F-FDG and ¹⁸F-Alfatide II, can be used to predict molecular subtypes of invasive breast cancer, especially for the luminal B (HER2 negative) subtype and the nonluminal subtype.

eIF6 is potential diagnostic and prognostic biomarker that associated with 18F-FDG PET/CT features and immune signatures in esophageal carcinoma

Background

Although eukaryotic initiation factor 6 (eIF6) is a novel therapeutic target, data on its importance in the development of esophageal carcinoma (ESCA) remains limited. This study evaluated the correlation between eIF6 expression and metabolic analysis using fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) -Positron emission tomography (PET) and immune gene signatures in ESCA.

Methods

This study employed The Cancer Genome Atlas (TCGA) to analyze the expression and prognostic value of eIF6, as well as its relationship with the immune gene signatures in ESCA patients. The qRT-PCR and Western blot analyses were used to profile the expression of eIF6 in ESCA tissues and different ESCA cell lines. The expression of tumor eIF6 and glucose transporter 1 (GLUT1) was examined using immunohistochemical tools in fifty-two ESCA patients undergoing routine ¹⁸F-FDG PET/CT before surgery. In addition, the cellular responses to eIF6 knockdown in human ESCA cells were assessed via the MTS, EdU, flow cytometry and wound healing assays.

Results

Our data demonstrated that compared with the normal esophageal tissues, eIF6 expression was upregulated in ESCA tumor tissues and showed a high diagnostic value with an area under curve of 0.825 for predicting ESCA. High eIF6 expression was significantly correlated with shorter overall survival of patients with esophagus adenocarcinoma (p = 0.038), but not in squamous cell carcinoma of the esophagus (p = 0.078). In addition, tumor eIF6 was significantly associated with ¹⁸F-FDG PET/CT parameters: maximal and mean standardized uptake values (SUVmax and SUVmean) and total lesion glycolysis (TLG) (rho = 0.458, 0.460, and 0.300, respectively, p < 0.01) as well as GLUT1 expression (rho = 0.453, p < 0.001). A SUVmax cutoff of 18.2 led to prediction of tumor eIF6 expression with an accuracy of 0.755. Functional analysis studies demonstrated that knockdown of eIF6 inhibited ESCA cell growth and migration, and fueled cell apoptosis. Moreover, the Bulk RNA gene analysis revealed a significant inverse association between eIF6 and the tumor-infiltrating immune cells (macrophages, T cells, or Th1 cells) and immunomodulators in the ESCA microenvironment.

Conclusion

Our study suggested that eIF6 might serve as a potential prognostic biomarker associated with metabolic variability and immune gene signatures in ESCA tumor microenvironment.

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.

Synergistic impact of motion and acquisition/reconstruction parameters on 18 F-FDG PET radiomic features in non-small cell lung cancer: phantom and clinical studies

Objectives

This study is aimed at examining the synergistic impact of motion and acquisition/reconstruction parameters on ¹⁸F‐FDG PET image radiomic features in non‐small cell lung cancer (NSCLC) patients, and investigating the robustness of features performance in differentiating NSCLC histopathology subtypes.

Methods

An in‐house developed thoracic phantom incorporating lesions with different sizes was used with different reconstruction settings, including various reconstruction algorithms, number of subsets and iterations, full‐width at half‐maximum of post‐reconstruction smoothing filter and acquisition parameters, including injected activity and test–retest with and without motion simulation. To simulate motion, a special motor was manufactured to simulate respiratory motion based on a normal patient in two directions. The lesions were delineated semi‐automatically to extract 174 radiomic features. All radiomic features were categorized according to the coefficient of variation (COV) to select robust features. A cohort consisting of 40 NSCLC patients with adenocarcinoma (n = 20) and squamous cell carcinoma (n = 20) was retrospectively analyzed. Statistical analysis was performed to discriminate robust features in differentiating histopathology subtypes of NSCLC lesions.

Results

Overall, 29% of radiomic features showed a COV ≤5% against motion. Forty‐five percent and 76% of the features showed a COV ≤ 5% against the test–retest with and without motion in large lesions, respectively. Thirty‐three percent and 45% of the features showed a COV ≤ 5% against different reconstruction parameters with and without motion, respectively. For NSCLC histopathological subtype differentiation, statistical analysis showed that 31 features were significant (p‐value < 0.05). Two out of the 31 significant features, namely, the joint entropy of GLCM (AUC = 0.71, COV = 0.019) and median absolute deviation of intensity histogram (AUC = 0.7, COV = 0.046), were robust against the motion (same reconstruction setting).

Conclusions

Motion, acquisition, and reconstruction parameters significantly impact radiomic features, just as their synergies. Radiomic features with high predictive performance (statistically significant) in differentiating histopathological subtype of NSCLC may be eliminated due to non‐reproducibility.

Correlation of primary tumor metabolic parameters with clinical, histopathological and molecular characteristics in breast cancer patients at pre-operative staging FDG-PET/CT study

Background

The aim of this prospective study was to evaluate the correlation of primary tumor metabolic activity parameters; maximum standardized uptake value (SUVmax) and tumor SUVmax/liver average SUV ratio (TLR) with clinical, histopathological and molecular characteristics of initial staging breast cancer (BC) patients using 18F-fluorodeoxyglucose (FDG) positron emission tomography / computerized tomography (PET/CT) scan.

Results

Forty female patients with newly diagnosed BC were enrolled in our study, age ranging from 31-78 years (mean 50.5 +/- SD11.7).

All the primary tumors were detected with mean SUVmax 10.8(+/-SD 7.9). The mean /median SUVmax values of primary tumor was higher in premenopausal , stage III and IV, Estrogen Receptors negative( ER-), Progesterone Receptors negative(PR-), Human epidermal growth factor receptor 2 positive ( Her2neu+) patients, high nuclear grade (GIII), triple negative molecular subgroup (TN) and positive axillary lymph node (ALNs) metastasis,(P= 0.003, 0.017, 0.113, 0.089 0.01 ,0.002 , 0.007 and 0.016 respectively).

The mean/median TLR values was higher in premenopausal ,Her2neu+, GIII, TN molecular subtype patients, stage III and IV and in patients with positive ALNs , ER- and PR - patients (P= 0.002, 0.0476 , 0.005 , 0.018 , 0.039 and 0.022, 0.095 and 0.129 respectively).

SUVmax of the primary lesion and TLR were moderately negatively correlated with the age of the patients (P= 0.005 and 0.008 respectively), also they were moderately positively correlated with the size of the primary tumor (P= 0.019 and 0.036 respectively). TLR was predictive of nodal involvement AUC= 0.612 (95% CI: 0.431-792). The overall sensitivity and specificity of PET/CT for axillary staging was 100 % and 60 %, respectively (P= 0.006).

Conclusion

The SUVmax of the primary tumor and TLR values had similar significant associations with different prognostic factors in BC but only TLR can predict nodal involvement.

Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning

BACKGROUND

Human epidermal growth factor receptor 2 (HER2) expression status determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The purpose of this study was to evaluate the role of radiomics and machine learning based on PET/CT images in HER2 status prediction, and to identify the most effective combination of machine learning model and radiomic features.

METHODS

A total of 217 BC patients who underwent PET/CT examination were involved in the study and randomly divided into a training set (n = 151) and a testing set (n = 66). For all four models, the model parameters were determined using a threefold cross-validation in the training set. Each model's performance was evaluated on the independent testing set using the receiver operating characteristic (ROC) curve, and AUC was calculated to get a quantified performance measurement of each model.

RESULTS

Among the four developed machine learning models, the XGBoost model outperformed other machine learning models in HER2 status prediction. Furthermore, compared to the XGBoost model based on PET alone or CT alone radiomic features, the predictive power for HER2 status by using XGBoost model based on PET/CTmean or PET/CTconcat radiomic fusion features was dramatically improved with an AUC of 0.76 (95% confidence interval [CI] 0.69-0.83) and 0.72 (0.65-0.80), respectively.

CONCLUSIONS

The established machine learning classifier based on PET/CT radiomic features is potentially predictive of HER2 status in BC.

Radiomics, aptamers and nanobodies: New insights in cancer diagnostics and imaging

At present, cancer is a major health issue and the second leading cause of mortality worldwide. Researchers have been working hard on investigating not only improved therapeutics but also on early detection methods, both critical to increasing treatment efficacy and developing methods for disease prevention. Diagnosis of cancers at an early stage can promote timely medical intervention and effective treatment and will result in inhibiting tumor growth and development. Several advances have been made in the diagnostics and imagining technologies for early tumor detection and deciding an effective therapy these include radiomics, nanobodies, and aptamers. Here in this review, we summarize the main applications of radiomics, aptamers, and the use of nanobody-based probes for molecular imaging applications in diagnosis, treatment planning, and evaluations in the field of oncology to develop quantitative and personalized medicine. The preclinical data reported to date are quite promising, and it is predicted that nanobody-based molecular imaging agents will play an important role in the diagnosis and management of different cancer types in near future.

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer

Background

An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE).

Methods

A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status.

Results

Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485).

Conclusions

The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers

Introduction

We aimed to investigate whether ¹⁸F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS).

Materials and methods

On 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUV_max, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables.

Results

For ER expression, correlations were mainly observed with ¹⁸F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness__ER and uniformity__HISTO (ρ = −0.386, p = 0.017) and correlation__PR and entropy__GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUV_max, skewness__ER, kurtosis__ER, entropy__HISTO, and uniformity__HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness__ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness__ER, none of the parameters were independent predictors. Indeed, skewness__ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023).

Conclusion

A heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

Artificial intelligence applications for oncological positron emission tomography imaging

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?

OBJECTIVES

This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs).

METHODS

Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis.

RESULTS

In all, 843 radiomics features per case were obtained from expert-delineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility.

CONCLUSIONS

This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.

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.

PIK3CA Mutational Status Is Associated with High Glycolytic Activity in ER+/HER2- Early Invasive Breast Cancer: a Molecular Imaging Study Using [18F]FDG PET/CT

PURPOSE

In PIK3CA mutant breast cancer, downstream hyperactivation of the PI3K/AKT/mTOR pathway may be associated with increased glycolysis of cancer cells. The purpose of this study was to investigate the functional association of PIK3CA mutational status and tumor glycolysis in invasive ER+/HER2- early breast cancer.

PROCEDURES

This institutional review board-approved retrospective study included a dataset of 67 ER+/HER2- early breast cancer patients. All patients underwent 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/X-ray computed tomography ([¹⁸F]FDG PET/CT) and clinico-pathologic assessments as part of a prospective study. For this retrospective analysis, pyrosequencing was used to detect PIK3CA mutations of exons 4, 7, 9, and 20. Tumor glucose metabolism was assessed semi-quantitatively with [¹⁸F]FDG PET/CT using maximum standardized uptake values (SUV_max). SUV_max values were corrected for the partial volume effect, and metabolic tumor volume was calculated using the volume of interest automated lesion growing function 2D tumor size, i.e., maximum tumor diameter was assessed on concurrent pre-treatment contrast-enhanced magnetic resonance imaging.

RESULTS

PIK3CA mutations were present in 45 % of all tumors. Mutations were associated with a small tumor diameter (p < 0.01) and with low nuclear grade (p = 0.04). Glycolytic activity was positively associated with nuclear grade (p = 0.01), proliferation (p = 0.002), regional lymph node metastasis (p = 0.015), and metabolic tumor volume (p = 0.001) but not with tumor size/T-stage. In invasive ductal carcinomas, median SUV_max was increased in PIK3CA-mutated compared to wild-type tumors; however, this increase did not reach statistical significance (p = 0.05). Multivariate analysis of invasive ductal carcinomas revealed [¹⁸F]FDG uptake to be independently associated with PIK3CA status (p = 0.002) and nuclear tumor grade (p = 0.046). Size, volume, and regional nodal status had no influence on glycolytic activity. PIK3CA mutational status did not influence glycolytic metabolism in lobular carcinomas. Glycolytic activity and PIK3CA mutational status had no significant influence on recurrence-free survival or disease-specific survival.

CONCLUSIONS

In ER+/HER2- invasive ductal carcinomas of the breast, glucose uptake is independently associated with PIK3CA mutations. Initial data suggest that [¹⁸F]FDG uptake reflects complex genomic alterations and may have the potential to be used as candidate biomarker for monitoring therapeutic response and resistance mechanisms in emerging therapies that target the PI3K/AKT/mTOR pathway.

Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation?

The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.

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.

Breast Cancer Radiogenomics: Current Status and Future Directions

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.

Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features

Objective

To evaluate the human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer using multidetector computed tomography (MDCT)-based handcrafted and deep radiomics features.

Methods

This retrospective study enrolled 339 female patients (primary cohort, n=177; validation cohort, n=162) with pathologically confirmed invasive breast cancer. Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase. After the feature selection procedures, handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis. Performance was assessed by measures of discrimination, calibration, and clinical usefulness in the primary cohort and validated in the validation cohort.

Results

The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739 [95% confidence interval (95% CI): 0.661−0.818] in the primary cohort and 0.695 (95% CI: 0.609−0.781) in the validation cohort. The deep radiomics signature also had a discriminative ability with a C-index of 0.760 (95% CI: 0.690−0.831) in the primary cohort and 0.777 (95% CI: 0.696−0.857) in the validation cohort. The combined model, which incorporated both the handcrafted and deep radiomics signatures, showed good discriminative ability with a C-index of 0.829 (95% CI: 0.767−0.890) in the primary cohort and 0.809 (95% CI: 0.740−0.879) in the validation cohort.

Conclusions

Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer. Thus, these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling.

BACKGROUND

Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype.

METHODS

We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype.

RESULTS

We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone.

CONCLUSION

Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.

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.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

18F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia

¹⁸F-FDG PET / CT is used clinically for the detection of extramedullary lesions in patients with relapsed acute leukemia (AL). However, the visual analysis of ¹⁸F-FDG diffuse bone marrow uptake in detecting bone marrow involvement (BMI) in routine clinical practice is still challenging. This study aims to improve the diagnostic performance of ¹⁸F-FDG PET/CT in detecting BMI for patients with suspected relapsed AL.

Methods: Forty-one patients (35 in training group and 6 in independent validation group) with suspected relapsed AL were retrospectively included in this study. All patients underwent both bone marrow biopsy (BMB) and ¹⁸F-FDG PET/CT within one week. The BMB results were used as the gold standard or real “truth” for BMI. The bone marrow ¹⁸F-FDG uptake was visually diagnosed as positive and negative by three nuclear medicine physicians. The skeletal volumes of interest were manually drawn on PET/CT images. A total of 781 PET and 1045 CT radiomic features were automatically extracted to provide a more comprehensive understanding of the embedded pattern. To select the most important and predictive features, an unsupervised consensus clustering method was first performed to analyze the feature correlations and then used to guide a random forest supervised machine learning model for feature importance analysis. Cross-validation and independent validation were conducted to justify the performance of our model.

Results: The training group involved 16 BMB positive and 19 BMB negative patients. Based on the visual analysis of ¹⁸F-FDG PET, 3 patients had focal uptake, 8 patients had normal uptake, and 24 patients had diffuse uptake. The sensitivity, specificity, and accuracy of visual analysis for BMI diagnosis were 62.5%, 73.7%, and 68.6%, respectively. With the cross-validation on the training group, the machine learning model correctly predicted 31 patients in BMI. The sensitivity, specificity, and accuracy of the machine learning model in BMI detection were 87.5%, 89.5%, and 88.6%, respectively, significantly higher than the ones in visual analysis (P < 0.05). The evaluation on the independent validation group showed that the machine learning model could achieve 83.3% accuracy.

Conclusions:¹⁸F-FDG PET/CT radiomic analysis with machine learning model provided a quantitative, objective and efficient mechanism for identifying BMI in the patients with suspected relapsed AL. It is suggested in particular for the diagnosis of BMI in the patients with ¹⁸F-FDG diffuse uptake patterns.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method

It is difficult to accurately assess axillary lymph nodes metastasis and the diagnosis of axillary lymph nodes in patients with breast cancer is invasive and has low-sensitivity preoperatively. This study aims to develop a mammography-based radiomics nomogram for the preoperative prediction of ALN metastasis in patients with breast cancer. This study enrolled 147 patients with clinicopathologically confirmed breast cancer and preoperative mammography. Features were extracted from each patient's mammography images. The least absolute shrinkage and selection operator regression method was used to select features and build a signature in the primary cohort. The performance of the signature was assessed using support vector machines. We developed a nomogram by incorporating the signature with the clinicopathologic risk factors. The nomogram performance was estimated by its calibration ability in the primary and validation cohorts. The signature was consisted of 10 selected ALN-status-related features. The AUC of the signature from the primary cohort was 0.895 (95% CI, 0.887-0.909) and 0.875 (95% CI, 0.698-0.891) for the validation cohort. The C-Index of the nomogram from the primary cohort was 0.779 (95% CI, 0.752-0.793) and 0.809 (95% CI, 0.794-0.833) for the validation cohort. Our nomogram is a reliable and non-invasive tool for preoperative prediction of ALN status and can be used to optimize current treatment strategy for breast cancer patients.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

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.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Selected PET radiomic features remain the same

Purpose

We investigated whether PET radiomic features are affected by differences in the scanner, scan protocol, and lesion location using ¹⁸F-FDG PET/CT and PET/MR scans.

Results

SUV, TMR, skewness, kurtosis, entropy, and homogeneity strongly correlated between PET/CT and PET/MR images. SUVs were significantly higher on PET/MR_{0-2 min} and PET/MR_{0-10 min} than on PET/CT in gynecological cancer (p = 0.008 and 0.008, respectively), whereas no significant difference was observed between PET/CT, PET/MR_{0–2 min}, and PET/MR_{0–10 min} images in oral cavity/oropharyngeal cancer. TMRs on PET/CT, PET/MR_{0–2 min}, and PET/MR_{0–10 min} increased in this order in gynecological cancer and oral cavity/oropharyngeal cancer. In contrast to conventional and histogram indices, 4 textural features (entropy, homogeneity, SRE, and LRE) were not significantly different between PET/CT, PET/MR_{0–2 min}, and PET/MR_{0–10 min} images.

Conclusions

¹⁸F-FDG PET radiomic features strongly correlated between PET/CT and PET/MR images. Dixon-based attenuation correction on PET/MR images underestimated tumor tracer uptake more significantly in oral cavity/oropharyngeal cancer than in gynecological cancer. ¹⁸F-FDG PET textural features were affected less by differences in the scanner and scan protocol than conventional and histogram features, possibly due to the resampling process using a medium bin width.

Methods

Eight patients with gynecological cancer and 7 with oral cavity/oropharyngeal cancer underwent a whole-body ¹⁸F-FDG PET/CT scan and regional PET/MR scan in one day. PET/MR scans were performed for 10 minutes in the list mode, and PET/CT and 0–2 min and 0–10 min PET/MR images were reconstructed. The standardized uptake value (SUV), tumor-to-muscle SUV ratio (TMR), skewness, kurtosis, entropy, homogeneity, short-run emphasis (SRE), and long-run emphasis (LRE) were compared between PET/CT, PET/MR_{0-2 min}, and PET/MR_{0-10 min} images.

Rapid review: radiomics and breast cancer

PURPOSE

To perform a rapid review of the recent literature on radiomics and breast cancer (BC).

METHODS

A rapid review, a streamlined approach to systematically identify and summarize emerging studies was done (updated 27 September 2017). Clinical studies eligible for inclusion were those that evaluated BC using a radiomics approach and provided data on BC diagnosis (detection or characterization) or BC prognosis (response to therapy, morbidity, mortality), or provided data on technical challenges (software application: open source, repeatability of results). Descriptive statistics, results, and radiomics quality score (RQS) are presented.

RESULTS

N = 17 retrospective studies, all published after 2015, provided BC-related radiomics data on 3928 patients evaluated with a radiomics approach. Most studies were done for diagnosis and/or characterization (65%, 11/17) or to aid in prognosis (41%, 7/17). The mean number of radiomics features considered was 100. Mean RQS score was 11.88 ± 5.8 (maximum value 36). The RQS criteria related to validation, gold standard, potential clinical utility, cost analysis, and open science data had the lowest scores. The majority of studies n = 16/17 (94%) provided correlation with histological outcomes and staging variables or biomarkers. Only 4/17 (23%) studies provided evidence of correlation with genomic data. Magnetic resonance imaging (MRI) was used in most studies n = 14/17 (82%); however, ultrasound (US), mammography, or positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F FDG PET/CT) was also used. Much heterogeneity was found for software usage.

CONCLUSIONS

The study of radiomics in BC patients is a new and emerging translational research topic. Radiomics in BC is frequently done to potentially improve diagnosis and characterization, mostly using MRI. Substantial quality limitations were found; high-quality prospective and reproducible studies are needed to further potential application.