Assessment Of Tumor Heterogeneity Research Articles

Molecular imaging of renal cell carcinomas: ready for prime time.

The clinical diagnosis of renal cell carcinoma (RCC) is constantly evolving. Diagnostic imaging of RCC relying on enhanced computed tomography (CT) and magnetic resonance imaging (MRI) is commonly used for renal mass characterization and assessment of tumour thrombosis, whereas pathology is the gold standard for establishing diagnosis. However, molecular imaging is rapidly improving the clinical management of RCC, particularly clear-cell RCC. Molecular imaging aids in the non-invasive visualization and characterization of specific biomarkers such as carbonic anhydrase IX and CD70 within the tumours, which help to assess tumour heterogeneity and status. Target-specific molecular imaging of RCCs will substantially improve the diagnostic landscape of RCC and will further facilitate clinical decision-making regarding initial staging and re-staging, monitoring of recurrence and metastasis, patient stratification and selection, and the prediction and evaluation of treatment responses.

Liquid Biopsy in the Clinical Management of Cancers.

Liquid biopsy, a noninvasive diagnosis that examines circulating tumor components in body fluids, is increasingly used in cancer management. An overview of relevant literature emphasizes the current state of liquid biopsy applications in cancer care. Biomarkers in liquid biopsy, particularly circulating tumor DNA (ctDNA), circulating tumor RNAs (ctRNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and other components, offer promising opportunities for early cancer diagnosis, treatment selection, monitoring, and disease assessment. The implementation of liquid biopsy in precision medicine has shown significant potential in various cancer types, including lung cancer, colorectal cancer, breast cancer, and prostate cancer. Advances in genomic and molecular technologies such as next-generation sequencing (NGS) and digital polymerase chain reaction (dPCR) have expanded the utility of liquid biopsy, enabling the detection of somatic variants and actionable genomic alterations in tumors. Liquid biopsy has also demonstrated utility in predicting treatment responses, monitoring minimal residual disease (MRD), and assessing tumor heterogeneity. Nevertheless, standardizing liquid biopsy techniques, interpreting results, and integrating them into the clinical routine remain as challenges. Despite these challenges, liquid biopsy has significant clinical implications in cancer management, offering a dynamic and noninvasive approach to understanding tumor biology and guiding personalized treatment strategies.

Tumor heterogeneity: how could we use it to achieve better clinical outcomes?

Differences in tumors related to location, tissue type, and histological subtype have been well documented for decades. Tumors are also molecularly very diverse. In this short review we describe the current classification schemes for tumor heterogeneity. We enlist the various drivers of tumor heterogeneity generation and comment on their clinical significance. New molecular techniques promise to assess tumor heterogeneity at affordable cost, so that these techniques can soon enter the clinic. While tumor heterogeneity currently represents a major unfavorable barrier in the field of oncology, it may also be a key in revolutionizing cancer diagnosis and treatment. Information regarding tumor heterogeneity has the potential to provide more thorough prognostic information, guide more efficacious combination treatment regimens, and lead to the development of novel therapeutic strategies and identification of new targets. For these gains to be realized, assessment of tumor heterogeneity needs to be incorporated into current diagnostic protocols but standardized and reproducible assessment methods are required. Fortunately, when these advances are realized, tumor heterogeneity has the potential to improve clinical outcomes.

Review on Early Ovarian Carcinoma using Radiomics

Ovarian cancer remains one of the deadliest gynecological malignancies in the world, despite major advances in chemotherapy and surgery. This, according to several research, is related to the considerable heterogeneity in treatment response and prognosis. Mostly, computational approaches for assessing tumour heterogeneity have been developed, which employ medical imaging and radio genomes to look at the whole tumour heterogeneity rather than just a single biopsy sample. This tumoral heterogeneity is not taken into consideration by standard imaging utilising CT or MRI, especially in late stages of peritoneal carcinomatosis. As a result, medical imaging has been recommended as a new field in the assessment of tumour heterogeneity, rather than single biopsy sample, to evaluate the entire tumour burden heterogeneity. In medical imaging there are four basic steps involved for an efficient and accurate detection of ovarian cancer at early stage. This review discusses about the detail evaluation on the various four phases involved in Medical imaging. Thus the review brings out a detail evaluation of Medical imaging undertaken at every stage of processing.
 Bangladesh J Obstet Gynaecol, 2022; Vol. 37(1): 39-46

Advances in Molecular Diagnostics for Oral and Oropharyngeal Cancers: Current Practices and Future Directions

ABSTRACT Oral and oropharyngeal cancers (OOCs) represent significant global health challenges due to their high morbidity and mortality rates. Early detection and precise diagnosis are crucial for improving patient outcomes. This review explores the latest advances in molecular diagnostics for OOC, focusing on current practices and future directions. Recent developments in molecular techniques, including next-generation sequencing, liquid biopsy, and biomarker discovery, have revolutionized the diagnostic landscape. We discuss the clinical applications of these technologies in identifying genetic mutations, detecting circulating tumor DNA, and assessing tumor heterogeneity. In addition, the review highlights the potential of emerging diagnostic tools such as artificial intelligence and machine learning in enhancing the accuracy and speed of OOC detection. Future perspectives emphasize the integration of multiomics approaches and the need for standardized protocols to facilitate the translation of molecular diagnostics into routine clinical practice. This comprehensive review provides insights into the evolving field of molecular diagnostics, aiming to guide researchers and clinicians in the quest for improved diagnostic strategies for OOCs.

Tumor heterogeneity and treatment response assessment using next generation imaging and liquid biopsy in patients with metastatic castration resistant prostate cancer receiving abiraterone (ANGELA): A single-center prospective observational trial.

5057 Background: Metastatic castration resistant prostate cancer (mCRPC) is a disease of intra-patient tumor heterogeneity which poses obstacles in treatment and response evaluation. With the development of next generation imaging (NGI) and liquid biopsy, precise assessment of heterogeneity has entered a new era. In this study, we used longitudinal NGI (68Ga-PSMA and 18F-FDG PET/CT) and circulating tumor DNA (ctDNA) testing to assess tumor heterogeneity and treatment response in mCRPC patients receiving abiraterone. Methods: ANGELA is a single-center prospective observational trial (NCT05188911). We recruited mCRPC patients who received abiraterone as initial hormonal treatment. Eligible patients should have a life expectancy >12 months. CtDNA testing and NGI were performed at baseline and after 12 weeks of abiraterone (Week 13). Conventional imaging and PSA testing were performed at Week 1 and every 12 weeks. Primary end-point of this study is intra-patient lesion heterogeneity evaluated by 3 dimensions: (1) Imaging heterogeneity: defined as at least 1 lesion with FDG+/PSMA- phenotype at baseline and/or Week 13; (2) Genomic heterogeneity: quantified by ctDNA fraction increase and mutational changes per gene between baseline and Week 13; (3) Response heterogeneity: defined as at least 1 new tumoral lesion with either PSMA or FDG abnormal uptake at Week 13. Secondary end-points are correlations of heterogeneity with conventional-evaluated progression-free survival (PFS) and 2-year overall survival (OS). Results: Between May 20, 2020 and Feb 02, 2021, we included 33 eligible patients in our study. FDG+/PSMA- tumoral lesion was observed in 7/33 (21.2%) and 8/33 (24.2%) of patients at baseline and Week 13, respectively. Notably, 5 of 8 patients with imaging heterogeneity at Week 13 had no FDG+/PSMA- lesion at baseline. For genomic heterogeneity, we detected ctDNA fraction increase in 4/33 patients (12.1%). In addition, we found new gene mutations detected in Week 13 ctDNA testing in 5/33 patients (15.2%), 2 of 5 had ctDNA fraction increase. For response heterogeneity, we observed 15/33 patients (45.5%) patients developed at least 1 new tumoral lesion (FDG+ or PSMA+) at Week 13. We correlated heterogeneity factors with prognosis, and found that detection of new mutated genes (P=0.005 and P<0.001), new PSMA+ lesion (P<0.001 and P<0.001), and new FDG+ lesion (P=0.008 and P<0.0001) at Week 13 were significant predictors of shorter PFS and decreased 2-year OS. Detailed biomarker study for response analysis is still in process. Conclusions: Intra-patient tumor heterogeneity was common among mCRPC patients receiving abiraterone. Combining liquid biopsy with NGI can monitor changes in tumor heterogeneity and indicate early combination therapy. Clinical trial information: NCT05188911 .

A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence.

Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity. Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s). The average recorded accuracy of the proposed automated classification system was equal to 0.86. The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.

Analytical and Clinical Validation of Cell-Free Circulating Tumor DNA Assay for the Estimation of Tumor Mutational Burden.

Ultra-deep sequencing to detect low-frequency mutations in circulating tumor-derived DNA (ctDNA) increases the diagnostic value of liquid biopsy. The demand for large ctDNA panels for comprehensive genomic profiling and tumor mutational burden (TMB) estimation is increasing; however, few ctDNA panels for TMB have been validated. Here, we designed a ctDNA panel with 531 genes, named TMB500, along with a technical and clinical validation. Synthetic reference cell-free DNA materials with predefined allele frequencies were sequenced in a total of 92 tests in 6 batches to evaluate the precision, linearity, and limit of detection of the assay. We used clinical samples from 50 patients with various cancers, 11 healthy individuals, and paired tissue samples. Molecular barcoding and data analysis were performed using customized pipelines. The assay showed high precision and linearity (coefficient of determination, r2 =0.87) for all single nucleotide variants, with a limit of detection of 0.24%. In clinical samples, the TMB500 ctDNA assay detected most variants present and absent in tissues, showing that ctDNA could assess tumor heterogeneity in different tissues and metastasis sites. The estimated TMBs correlated well between tissue and blood, except in 4 cases with extreme heterogeneity that showed very high blood TMBs compared to tissue TMBs. A pilot evaluation showed that the TMB500 assay could be used for disease monitoring. The TMB500 assay is an accurate and reliable ctDNA assay for many clinical purposes. It may be useful for guiding the treatment of cancers with diverse genomic profiles, estimating TMB in immune therapy, and disease monitoring.

Temporal Heterogeneity of HER2 Expression and Spatial Heterogeneity of 18F-FDG Uptake Predicts Treatment Outcome of Pyrotinib in Patients with HER2-Positive Metastatic Breast Cancer.

Simple SummaryTumor heterogeneity plays an important role in malignant behaviors and treatment responses. This study aimed to evaluate the temporal and spatial heterogeneity in clinical practice and investigate its impact on the treatment outcome of pyrotinib in patients with HER2-positive metastatic breast cancer. Temporal heterogeneity was evaluated by the discordance between primary and metastatic immunohistochemistry results. 18F-FDG uptake heterogeneity on baseline PET/CT scan was assessed to reflect spatial tumor heterogeneity among metastases. Our results showed that heterogeneous HER2 status between primary and metastatic lesions and spatial 18F-FDG uptake heterogeneity were predictive of poorer outcomes of pyrotinib treatment. The best method to evaluate tumor heterogeneity in clinical practice still needs to be identified. Temporal heterogeneity of HER2 expression and spatial heterogeneity of 18F-FDG uptake provided practically applicable methods to assess tumor heterogeneity and potential guidance for treatment decisions.Background: This study aimed to evaluate tumor heterogeneity of metastatic breast cancer (MBC) and investigate its impact on the efficacy of pyrotinib in patients with HER2-positive MBC. Methods: MBC patients who underwent 18F-FDG PET/CT before pyrotinib treatment were included. Temporal and spatial tumor heterogeneity was evaluated by the discordance between primary and metastatic immunohistochemistry (IHC) results and baseline 18F-FDG uptake heterogeneity (intertumoral and intratumoral heterogeneity indexes: HI-inter and HI-intra), respectively. Progression-free survival (PFS) was estimated by the Kaplan–Meier method and compared by a log-rank test. Results: A total of 572 patients were screened and 51 patients were included. In 36 patients with matched IHC results, 25% of them had HER2 status conversion. Patients with homogenous HER2 positivity had the longest PFS, followed by patients with gained HER2 positivity, while patients with HER2 negative conversion could not benefit from pyrotinib (16.8 vs. 13.7 vs. 3.6 months, p < 0.0001). In terms of spatial heterogeneity, patients with high HI-intra and HI-inter had significantly worse PFS compared to those with low heterogeneity (10.6 vs. 25.3 months, p = 0.023; 11.2 vs. 25.3 months, p = 0.040). Conclusions: Temporal heterogeneity of HER2 status and spatial heterogeneity of 18F-FDG uptake could predict the treatment outcome of pyrotinib in patients with HER2-positive MBC, which provide practically applicable methods to assess tumor heterogeneity and guidance for treatment decisions.

Abstract 772: Single cell whole genome sequencing to assess tumor heterogeneity in endometrial cancer

Abstract Introduction: Endometrial cancer (EC) is the most common gynecological malignancy among women in developed countries. In the United States, it is estimated that 66,570 new cases and 12,940 deaths will occur in 2021. EC is more common in white women, but black women have a worse prognosis with higher mortality rates. Molecular characteristics in tumor tissue have been reported for potential clinical utility. Integrated genomic characterization efforts have shown common molecular alterations occurring in oncogenic pathways from bulk molecular profiling of EC tumors. However, little is known about the tumor heterogeneity in endometrial cancer. Thus, using high resolution single cell technologies, we assessed tumor heterogeneity in endometrial cancers. Our preliminary study includes endometrial tumors from five (3 African Americans, 1 Hispanic/Latino, 1 Caucasian) cases. We utilize bulk whole exome sequencing and single cell whole genome sequencing copy number variation to study tumor heterogeneity. Frozen tumor sections were used to collect and sequence single nuclei utilizing the 10x ChromiumTM single cell copy number variation (scCNV). Illumina NovasEQ600 was used to sequence the single cell libraries. Results: 1,546 cells were sequenced across five tumors. Each tumor was visually inspected through hierarchical clustering from the scCNV data. Beyond describing the heterogeneity through chromosome or chromosome arm level gains and losses in samples that included single and mixed histology tumors, we assessed clonal populations suggesting apparently different clonal evolution within samples. Mutations in cancer related genes such as TP53, PTEN and PIK3CA among others suggested a clonal tumor cell stratification. The single cell whole genome data was compared to bulk exome sequencing data to determine the power of scCNV to detect clonal populations of tumor cells. Conclusions: Using varied histologies in five EC tumors, we performed DNA-CNV analysis using single cell sequencing. This level of resolution enhanced our analysis to provide clear evidence of heterogeneity in all tumors that were assessed in our study. We plan to further expand our sample size to study the degree of heterogeneity in endometrial cancer. Citation Format: Enrique Velazquez Villarreal, Lee D. Gibbs, Dana Mahinbakht, Diane M. Da Silva, Lynda D. Roman, David W. Craig, John D. Carpten. Single cell whole genome sequencing to assess tumor heterogeneity in endometrial cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 772.

Current and Future Perspectives of Cell-Free DNA in Liquid Biopsy.

A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.

New Cluster Analysis Method for Quantitative Dynamic Contrast-Enhanced MRI Assessing Tumor Heterogeneity Induced by a Tumor-Microenvironmental Ameliorator (E7130) Treatment to a Breast Cancer Mouse Model.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide insight into tumor perfusion. However, a method that can quantitatively measure the intra-tumor distribution of tumor voxel clusters with a distinct range of Ktrans and ve values remains insufficiently explored. Two-dimensional cluster analysis may quantify the distribution of a tumor voxel subregion with a distinct range of Ktrans and ve values in human breast cancer xenografts. Prospective longitudinal study. Twenty-two female athymic nude mice with MCF-7 xenograft, treated with E7130, a tumor-microenvironmental ameliorator, or saline. 9.4 Tesla, turbo rapid acquisition with relaxation enhancement, and spoiled gradient-echo sequences. We performed two-dimensional k-means clustering to identify tumor voxel clusters with a distinct range of Ktrans and ve values on Days 0, 2, and 5 after treatment, calculated the ratio of the number of tumor voxels in each cluster to the total number of tumor voxels, and measured the normalized distances defined as the ratio of the distance between each tumor voxel and the nearest tumor margin to a tumor radius. Unpaired t-tests, Dunnett's multiple comparison tests, and Chi-squared test were used. The largest and second largest clusters constituted 44.4% and 27.5% of all tumor voxels with cluster centroid values of Ktrans at 0.040 min-1 and 0.116 min-1 , and ve at 0.131 and 0.201, respectively. At baseline (Day 0), the average normalized distances for the largest and second largest clusters were 0.33 and 0.24, respectively. E7130-treated group showed the normalized distance of the initial largest cluster decreasing to 0.25, while that of the second largest cluster increasing to 0.31. Saline-treated group showed no change. A two-dimensional cluster analysis might quantify the spatial distribution of a tumor subregion with a distinct range of Ktrans and ve values. 1 TECHNICAL EFFICACY: Stage 1.

Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer.

Simple SummaryMolecular characterization of circulating tumor DNA (ctDNA) can offer a window into tumor genetic heterogeneity, especially in metastatic cancers where different lesions may harbor different mutations. The presence of multiple tumor clones may be reflected by dispersed variant allele frequencies of mutations detected in a single ctDNA sample. We hypothesized that the degree of dispersion of somatic mutations detected with a targeted next-generation sequencing assay may correlate with clinical outcomes in metastatic colorectal cancer. We found that patients with high ctDNA-based tumor heterogeneity after first-line bevacizumab and chemotherapy had shorter progression-free survival and worse objective response. Plasma-based measurements of tumor heterogeneity may have prognostic value in various cancer types and should be further explored for assessing treatment response and other clinical applications.Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85–5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers.

High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome

Simple SummaryTumor heterogeneity can greatly influence therapy outcome and patient survival. In this study, we aimed at unraveling inter- and intra-patient heterogeneity of colorectal cancer liver metastases (CRLM). To this end, we comprehensively characterized CRLM using state-of-the-art high-throughput technologies combined with bioinformatics analyses. We found a high degree of inter- and intra-patient heterogeneity among the metastases, in particular in genes of the WNT and EGFR pathways. Through analyzing the master regulators and effectors associated with the regulation of these genes, we identified a specific gene signature that was highly expressed in a large cohort of colorectal cancer patients and associated with clinical outcome.Seventy percent of patients with colorectal cancer develop liver metastases (CRLM), which are a decisive factor in cancer progression. Therapy outcome is largely influenced by tumor heterogeneity, but the intra- and inter-patient heterogeneity of CRLM has been poorly studied. In particular, the contribution of the WNT and EGFR pathways, which are both frequently deregulated in colorectal cancer, has not yet been addressed in this context. To this end, we comprehensively characterized normal liver tissue and eight CRLM from two patients by standardized histopathological, molecular, and proteomic subtyping. Suitable fresh-frozen tissue samples were profiled by transcriptome sequencing (RNA-Seq) and proteomic profiling with reverse phase protein arrays (RPPA) combined with bioinformatic analyses to assess tumor heterogeneity and identify WNT- and EGFR-related master regulators and metastatic effectors. A standardized data analysis pipeline for integrating RNA-Seq with clinical, proteomic, and genetic data was established. Dimensionality reduction of the transcriptome data revealed a distinct signature for CRLM differing from normal liver tissue and indicated a high degree of tumor heterogeneity. WNT and EGFR signaling were highly active in CRLM and the genes of both pathways were heterogeneously expressed between the two patients as well as between the synchronous metastases of a single patient. An analysis of the master regulators and metastatic effectors implicated in the regulation of these genes revealed a set of four genes (SFN, IGF2BP1, STAT1, PIK3CG) that were differentially expressed in CRLM and were associated with clinical outcome in a large cohort of colorectal cancer patients as well as CRLM samples. In conclusion, high-throughput profiling enabled us to define a CRLM-specific signature and revealed the genes of the WNT and EGFR pathways associated with inter- and intra-patient heterogeneity, which were validated as prognostic biomarkers in CRC primary tumors as well as liver metastases.

Quantifying Tumor Heterogeneity from Multiparametric Magnetic Resonance Imaging of Prostate Using Texture Analysis.

Simple SummaryProstate cancer (PCa) occurs in males at a rate of 21.8%, predominantly at the customary primary site. High cure rates are possible through early detection and therapy when the tumor is still restricted to the prostate. These tumors do not grow rapidly, allowing for periods of up to 20 years between diagnosis and death. Multiparametric MRI (mp-MRI) is used as a non-invasive approach to diagnose PCa in subjects. This imaging method uses MR imaging with at least one functional MRI sequence to detect and characterize PCa. The use of multiparametric magnetic resonance imaging has refined the diagnosis of prostate cancer in radiology. Malignancy-modified critical features in tissue composition, such as heterogeneity, are associated with adverse tumor biology. Heterogeneity can be quantified through texture analysis, an effective technique for reviewing tumor images acquired in routine clinical practice. This study focused on identifying and quantifying tumor heterogeneity from prostate mp-MRI utilizing texture analysis.(1) Background: Multiparametric MRI (mp-MRI) is used to manage patients with PCa. Tumor identification via irregular sampling or biopsy is problematic and does not allow the comprehensive detection of the phenotypic and genetic alterations in a tumor. A non-invasive technique to clinically assess tumor heterogeneity is also in demand. We aimed to identify tumor heterogeneity from multiparametric magnetic resonance images using texture analysis (TA). (2) Methods: Eighteen patients with prostate cancer underwent mp-MRI scans before prostatectomy. A single radiologist matched the histopathology report to single axial slices that best depicted tumor and non-tumor regions to generate regions of interest (ROIs). First-order statistics based on the histogram analysis, including skewness, kurtosis, and entropy, were used to quantify tumor heterogeneity. We compared non-tumor regions with significant tumors, employing the two-tailed Mann–Whitney U test. Analysis of the area under the receiver operating characteristic curve (ROC-AUC) was used to determine diagnostic accuracy. (3) Results: ADC skewness for a 6 × 6 px filter was significantly lower with an ROC-AUC of 0.82 (p = 0.001). The skewness of the ADC for a 9 × 9 px filter had the second-highest result, with an ROC-AUC of 0.66; however, this was not statistically significant (p = 0.08). Furthermore, there were no substantial distinctions between pixel filter size groups from the histogram analysis, including entropy and kurtosis. (4) Conclusions: For all filter sizes, there was poor performance in terms of entropy and kurtosis histogram analyses for cancer diagnosis. Significant prostate cancer may be distinguished using a textural feature derived from ADC skewness with a 6 × 6 px filter size.

Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment.

Radiomic analysis provides information on the underlying tumour heterogeneity in lymphoma, reflecting the real-time evolution of malignancy. 2-Deoxy-2-[18F] fluoro-D-glucose positron emission tomography ([18F] FDG PET/CT) imaging is recommended before, during, and at the end of treatment for almost all lymphoma patients. This methodology offers high specificity and sensitivity, which can aid in accurate staging and assist in prompt treatment. Pretreatment [18F] FDG PET/CT-based radiomics facilitates improved diagnostic ability, guides individual treatment regimens, and boosts outcome prognosis based on heterogeneity as well as the biological, pathological, and metabolic status of the lymphoma. This technique has attracted considerable attention given its numerous applications in medicine. In the current review, we will briefly describe the basic radiomics workflow and types of radiomic features. Details of current applications of baseline [18F] FDG PET/CT-based radiomics in lymphoma will be discussed, such as differential diagnosis from other primary malignancies, diagnosis of bone marrow involvement, and response and prognostic prediction. We will also describe how this technique provides a unique noninvasive platform to assess tumour heterogeneity. Newly emerging PET radiotracers and multimodality technology will improve diagnostic specificity and further clarify tumor biology and even genetic variations in lymphoma, potentially promoting the development of precision medicine.

Assessment of Tumor Heterogeneity in High-Grade Serous Ovarian Cancer: Mass Cytometry to Understand the Complex Tumor Biology.

Ovarian cancer (OC) is the most deadly gynecological malignancy worldwide. OC patients undergo debulking surgery followed by platinum/taxane-based chemotherapy; however, despite recent development of new therapeutic approaches based on combination of chemotherapy and innovative targeted-therapies, most of them relapse due to chemoresistance. Many studies have been carried out to decipher the high heterogeneity of ovarian cancer cells that drives tumor treatment failure. Here, we describe our experience in the characterization of ovarian cancer cell subsets through a high-resolution technology in multiparametric analysis, such as mass cytometry (MC).

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients.

Simple SummaryTumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine.This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Monoclonal antibody based radiopharmaceuticals for imaging and therapy

The concept of personalized medicine has been steadily growing for the past decades. Monoclonal antibodies (mAbs) are undoubtedly playing an important role in the transition away from conventional medical practice to a more tailored approach to deliver the best therapy with the highest safety margin to a specific patient. In certain instances, mAbs and antibody drug conjugates (ADCs) may represent the preferred therapeutic option for several types of cancers due to their high specificity and affinity to the antigen. Monoclonal antibodies can be labeled with specific radionuclides well-suited for PET (Positron Emission Tomography) or gamma camera scintigraphy. The use of radiolabeled mAbs allows the interrogation of specific biomarkers and assessment of tumor heterogeneity in vivo by a single diagnostic imaging scan that includes the whole-body in the field-of-view. Moreover, the same mAb can then be radiolabeled with an analogous radionuclide for the delivery of beta-minus radiation or alpha-particles as part of a radioimmunotherapy (RIT) approach. However, the path to develop, validate, and implement mAb-based radiopharmaceuticals from bench-to-bedside is complex due to the extensive pre-clinical experiments and toxicological studies required, and the necessity of labor-intensive clinical trials that often require multi-time-point imaging and blood draws for internal radiation dosimetry and pharmacokinetics. As more mAb-based radiopharmaceuticals have been developed and evaluated, the opportunities and limitations offered by mAbs have become better defined. Our aim with this manuscript is therefore to provide an overview of the recent advances in the development of mAb-based radiopharmaceuticals and their clinical applications in Oncology.

Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques

BackgroundTexture analysis has been increasingly used in the field of positron emission tomography (PET)/computed tomography (CT) imaging with Fluorine-18 fluorodeoxyglucose (18F-FDG), aiming at assessing tumor heterogeneity. The purpose of the present study is to examine the feasibility of performing texture analysis in carotid arteries, investigate the value of textural features as predictors of potential plaque vulnerability using as reference standards histological and immunohistochemical data and compare their performance with conventional uptake measurements. Methods67 different 18F-FDG PET-based textural features were extracted from carotid images of 21 patients with high-grade carotid stenosis undergoing endarterectomy. To identify the more reliable predictors, univariate logistic regression analysis was performed. The accuracy was satisfactory in case of an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) ≥ 0.80. ResultsFirst measure of information correlation (AUC = 0.87, P < 0.001), large zone low gray level emphasis (AUC = 0.87, P < 0.001), and normalized run length non-uniformity (AUC = 0.84, P < 0.001) were the most optimal textural features for identifying characteristics of plaque vulnerability based on histological analysis. Addition of textural features to target-to-background ratio (TBR) (AUC = 0.74, P = 0.031) resulted in an AUC = 0.92 (P < 0.001), however, this did not reach statistical significance (Pdiff = 0.09). Intensity histogram standard deviation (AUC = 0.87, P < 0.001) and joint variance (AUC = 0.81, P = 0.001) were the most efficient features for signal differential in relation to immunohistochemical findings and provided incremental value compared to TBR (Pdiff = 0.02). ConclusionTexture analysis can be applied in 18F-FDG PET carotid imaging providing valuable information for plaque characterization.