Radiomics and circulating tumor cells: personalized care in hepatocellular carcinoma?

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Prognostic value of genotyping in hepatocellular carcinoma: A systematic review

Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in sequencing technology are opening genomics to widespread application for diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of genotyping for risk stratification and prognostication of HCC. We performed a systematic review of manuscripts published on MEDLINE from 1 January 2009 to 1 January 2022, addressing the value of genotyping for HCC risk stratification and prognostication. Publication information for each has been collected using a standardized data extraction form. Twenty-five articles were analysed. This study showed that various genomics approaches (i.e., NGS, SNP, CASP or polymorphisms in circadian genes' association) provided predictive and prognostic information, such as disease control rate, median progression-free survival, and shorter median overall survival. Genotyping, which advances in understanding the molecular origin, could be a solution to predict prognosis or treatment response in patients with HCC.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery

PURPOSE

To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively.

MATERIALS AND METHODS

A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.

RESULTS

The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.

CONCLUSION

Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.

Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma

Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV melanoma. ctDNAmaf and radiomic observations were obtained from 15 patients with a total of 70 CT examinations acquired as part of a prospective trial. 26 of 39 radiomic features showed a significant relationship with log(ctDNAmaf). Principal component analysis was used to define a radiomics signature that predicted ctDNAmaf independent of lesion volume. This radiomics signature and serum lactate dehydrogenase were independent predictors of ctDNAmaf. Together, these results suggest that radiomic features and ctDNAmaf may serve as complementary clinical tools for treatment monitoring.

Discovery and Validation of a CT-Based Radiomic Signature for Preoperative Prediction of Early Recurrence in Hypopharyngeal Carcinoma

Purpose

In the clinical management of hypopharyngeal squamous cell carcinoma (HSCC), preoperative identification of early recurrence (≤2 years) after curative resection is essential. Thus, we aimed to develop a CT-based radiomic signature to predict early recurrence in HSCC patients preoperatively.

Methods

In total, 167 HSCC patients who underwent partial surgery were enrolled in this retrospective study and divided into two groups, i.e., the training cohort (n = 133) and the validation cohort (n = 34). Each individual was followed up for at least for 2 years. Radiomic features were extracted from CT images, and the radiomic signature was built with the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model. The associations of preoperative clinical factors with early recurrence were evaluated. A radiomic signature-combined model was built, and the area under the curve (AUC) was used to explore their performance in discriminating early recurrence.

Results

Among the 1415 features, 335 of them were selected using the variance threshold method. Then, the SelectKBest method was further used for the selection of 31 candidate features. Finally, 11 out of 31 optimal features were identified with the LASSO algorithm. In the LR classifier, the AUCs of the training and validation sets in discriminating early recurrence were 0.83 (95% CI: 0.76-0.90) (sensitivity 0.8 and specificity 0.83) and 0.83 (95% CI: 0.67-0.99) (sensitivity 0.69 and specificity 0.71), respectively.

Conclusions

Using the radiomic signature, we developed a radiomic signature to preoperatively predict early recurrence in patients with HSCC, which may serve as a potential noninvasive tool to guide personalized treatment.

A panel of five plasma proteins for the early diagnosis of hepatitis B virus-related hepatocellular carcinoma in individuals at risk

BACKGROUND

To improve the early diagnosis of hepatocellular carcinoma (HCC), more effective diagnostic biomarkers are needed. A combination of biomarkers is reported to distinguish individuals with early-stage HCC from at-risk individuals.

METHODS

Participants in this study were recruited from six hospitals in China. Literature review was used to choose 19 candidate proteins, a case-control study in the discovery stage was used to identify five proteins (P5) that constituted a diagnostic model. In the training and validation stages, the effectiveness of P5 for detecting early-stage HCC was tested (cross-sectional study). Finally, a nested case-control study independent of the other stages was set up to evaluate the P5 in the preclinical diagnosis of HCC.

FINDINGS

Between February 2013 and June 2017, a total of 1396 participants were recruited. A panel of 5 proteins (P5: OPN, GDF15, NSE, TRAP5 and OPG) showed high diagnostic accuracy when differentiating the early-stage HCC from the at-risk group, with AUCs of 0·892, 0·907 and 0·852 for the training stage, validation cohort 1 and cohort 2 data sets, respectively. In the prediction set, the sensitivity of P5 for diagnosing preclinical HCC increased with time, starting from 12 months before to the time of definitive clinical diagnosis (range, 46·15% to 86·67%).

INTERPRETATION

The P5 panel has the potential to screen populations at high risk of developing HCC and can enable the early diagnosis of HCC.

FUNDING

Research supported by grants from eight funds. All sources of funding were declared at the end of the text.

History and Evolution of Yttrium-90 Radioembolization for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and affects millions worldwide. Due to the lack of effective systemic therapies for HCC, researchers have been investigating the use of locoregional tumor control with Yttrium-90 (Y90) radioembolization since the 1960s. Following the development of glass and resin Y90 microspheres in the early 1990s, Y90 radioembolization has been shown to be a safe and efficacious treatment for patients with HCC across Barcelona Clinic Liver Cancer (BCLC) stages. By demonstrating durable local control, good long term outcomes, and equivalent if not superior tumor responses and tolerability when compared to alternative therapies including transarterial chemoembolization (TACE) and sorafenib, Y90 radioembolization is being increasingly used in HCC treatment. More recently, investigations into variations in Y90 radioembolization technique including radiation segmentectomy and radiation lobectomy have further expanded its clinical utility. Here, we discuss the history and evolution of Y90 use in HCC. We outline key clinical trials that have established the safety and efficacy of Y90 radioembolization, and also summarize trials comparing its efficacy to existing HCC treatments. We conclude by reviewing the techniques of radiation segmentectomy and lobectomy, and by discussing dosimetry.

Irreversible Electroporation in Liver Cancers and Whole Organ Engineering

Liver cancers contribute significantly to cancer-related mortality worldwide and liver transplants remain the cornerstone of curative treatment for select, early-stage patients. Unfortunately, because of a mismatch between demand and supply of donor organs, liver cancer patients must often wait extended periods of time prior to transplant. A variety of local therapies including surgical resection, transarterial chemoembolization, and thermal ablative methods exist in order to bridge to transplant. In recent years, a number of studies have examined the role of irreversible electroporation (IRE) as a non-thermal local ablative method for liver tumors, particularly for those adjacent to critical structures such as the vasculature, gall bladder, or bile duct. In addition to proving its utility as a local treatment modality, IRE has also demonstrated promise as a technique for donor organ decellularization in the context of whole-organ engineering. Through complete non-thermal removal of living cells, IRE allows for the creation of an acellular extra cellular matrix (ECM) scaffold that could theoretically be recellularized and implanted into a living host. Here, we comprehensively review studies investigating IRE, its role in liver cancer treatment, and its utility in whole organ engineering.

Liquid Biopsy in Gastrointestinal Cancers

Liquid biopsy is the sampling of any biological fluid in an effort to enrich and analyze a tumor's genetic material. Peripheral blood remains the most studied liquid biopsy material, with circulating tumor cells (CTC's) and circulating tumor DNA (ctDNA) allowing the examination and longitudinal monitoring of a tumors genetic landscape. With applications in cancer screening, prognostic stratification, therapy selection and disease surveillance, liquid biopsy represents an exciting new paradigm in the field of cancer diagnostics and offers a less invasive and more comprehensive alternative to conventional tissue biopsy. Here, we examine liquid biopsies in gastrointestinal cancers, specifically colorectal, gastric, and pancreatic cancers, with an emphasis on applications in diagnostics, prognostics and therapeutics.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis

Background

To assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC).

Methods

Thirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann–Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS).

Results

Uniformity (p < 0.001), entropy (p = 0.009), correlation (p < 0.001), and mean intensity (p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity (p = 0.045) and inverse difference normalized (p = 0.046) were associated with OS whereas tumor intensity (p = 0.366), tumor size (p = 0.611) and other textural features including uniformity (p = 0.334), entropy (p = 0.330) and correlation (p = 0.068) were not associated with OS.

Conclusion

CT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.

Deep Learning in Medical Imaging: General Overview

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules

A solitary pulmonary nodule is defined as radiographic lesion with diameters no more than 3 cm and completely surrounded by normal lung tissue. It is commonly encountered in clinical practice and its diagnosis is a big challenge. Medical imaging, as a non-invasive approach, plays a crucial role in the diagnosis of solitary pulmonary nodules since the potential morbidity of surgery and the limits of biopsy. Advanced hardware, image acquisition and analysis technologies have led to the utilization of imaging towards quantitative imaging. With the aim of mining more useful information from image data, radiomics with high-throughput extraction can play a useful role. This article is to introduce the current state of radiomics studies and describe the general procedures. Another objective of this paper is to discover the feasibility and potential of radiomics methods on differentiating solitary pulmonary nodules and to look into the future direction of radiomics in this area.