Shota Yamamoto

Percutaneous Ablation of Hepatocellular Carcinoma: Current Status

Hepatocellular carcinoma (HCC) is an increasingly common disease with dismal long-term survival. Percutaneous ablation has gained popularity as a minimally invasive, potentially curative therapy for HCC in nonoperative candidates. The seminal technique of percutaneous ethanol injection has been largely supplanted by newer modalities, including radiofrequency ablation, microwave ablation, cryoablation, and high-intensity focused ultrasound ablation. A review of these modalities, including technical success, survival rates, and complications, will be presented, as well as considerations for treatment planning and follow-up.

ALK Molecular Phenotype in Non–Small Cell Lung Cancer: CT Radiogenomic Characterization

PURPOSE To present a radiogenomic computed tomographic (CT) characterization of anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) (ALK+). MATERIALS AND METHODS In this HIPAA-compliant institutional review board-approved retrospective study, CT studies, ALK status, and clinical-pathologic data in 172 patients with NSCLC from three institutions were analyzed. A screen of 24 CT image traits was performed in a training set of 59 patients, followed by random forest variable selection incorporating 24 CT traits plus six clinical-pathologic covariates to identify a radiogenomic predictor of ALK+ status. This predictor was then validated in an independent cohort (n = 113). Test-for-accuracy and subset analyses were performed. A similar analysis was performed to identify a biomarker associated with shorter progression-free survival (PFS) after therapy with the ALK inhibitor crizotinib. RESULTS ALK+ status was associated with central tumor location, absence of pleural tail, and large pleural effusion. An ALK+ radiogenomic CT status biomarker consisting of these three imaging traits with patient age of younger than 60 years showed strong discriminatory power for ALK+ status, with a sensitivity of 83.3% (15 of 18), a specificity of 77.9% (74 of 95), and an accuracy of 78.8% (89 of 113) in independent testing. The discriminatory power was particularly strong in patients with operable disease (stage IIIA or lower), with a sensitivity of 100.0% (five of five), a specificity of 88.1% (37 of 42), and an accuracy of 89.4% (42 of 47). Tumors with a disorganized vessel pattern had a shorter PFS with crizotinib therapy than tumors without this trait (11.4 vs 20.2 months, P = .041). CONCLUSION ALK+ NSCLC has distinct characteristics at CT imaging that, when combined with clinical covariates, discriminate ALK+ from non-ALK tumors and can potentially identify patients with a shorter durable response to crizotinib.

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

PURPOSE To perform a radiogenomic analysis of women with breast cancer to study the multiscale relationships among quantitative computer vision-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging phenotypes, early metastasis, and long noncoding RNA (lncRNA) expression determined by means of high-resolution next-generation RNA sequencing. MATERIALS AND METHODS In this institutional review board-approved study, an automated image analysis platform extracted 47 computational quantitative features from DCE MR imaging data in a training set (n = 19) to screen for MR imaging biomarkers indicative of poor metastasis-free survival (MFS). The lncRNA molecular landscape of the candidate feature was defined by using an RNA sequencing-specific negative binomial distribution differential expression analysis. Then, this radiogenomic biomarker was applied prospectively to a validation set (n = 42) to allow prediction of MFS and lncRNA expression by using quantitative polymerase chain reaction analysis. RESULTS The quantitative MR imaging feature, enhancing rim fraction score, was predictive of MFS in the training set (P = .007). RNA sequencing analysis yielded an average of 55.7 × 10(6) reads per sample and identified 14 880 lncRNAs from a background of 189 883 transcripts per sample. Radiogenomic analysis allowed identification of three previously uncharacterized and five named lncRNAs significantly associated with high enhancing rim fraction, including Homeobox transcript antisense intergenic RNA (HOTAIR) (P < .05), a known predictor of poor MFS in patients with breast cancer. Independent validation confirmed the association of the enhancing rim fraction phenotype with both MFS (P = .002) and expression of four of the top five differentially expressed lncRNAs (P < .05), including HOTAIR. CONCLUSION The enhancing rim fraction score, a quantitative DCE MR imaging lncRNA radiogenomic biomarker, is associated with early metastasis and expression of the known predictor of metastatic progression, HOTAIR.

Image-guided tumor ablation: emerging technologies and future directions.

As the trend continues toward the decreased invasiveness of medical procedures, image-guided percutaneous ablation has begun to supplant surgery for the local control of small tumors in the liver, kidney, and lung. New ablation technologies, and refinements of existing technologies, will enable treatment of larger and more complex tumors in these and other organs. At the same time, improvements in intraprocedural imaging promise to improve treatment accuracy and reduce complications. In this review, the latest advancements in clinical and experimental ablation technologies will be summarized, and new applications of image-guided tumor ablation will be discussed.

Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer.

Purpose To investigate whether non-small cell lung cancer (NSCLC) tumors that express high normalized maximum standardized uptake value (SUVmax) are associated with a more epithelial-mesenchymal transition (EMT)-like phenotype. Materials and Methods In this institutional review board-approved study, a public NSCLC data set that contained fluorine 18 ((18)F) fluoro-2-deoxyglucose positron emission tomography (PET) and messenger RNA expression profile data (n = 26) was obtained, and patients were categorized on the basis of measured normalized SUVmax values. Significance analysis of microarrays was then used to create a radiogenomic signature. The prognostic ability of this signature was assessed in a second independent data set that consisted of clinical and messenger RNA expression data (n = 166). Signature concordance with EMT was evaluated by means of validation in a publicly available cell line data set. Finally, by establishing an in vitro EMT lung cancer cell line model, an attempt was made to substantiate the radiogenomic signature with quantitative polymerase chain reaction, and functional assays were performed, including Western blot, cell migration, glucose transporter, and hexokinase assays (paired t test), as well as pharmacologic assays against chemotherapeutic agents (half-maximal effective concentration). Results Differential expression analysis yielded a 14-gene radiogenomic signature (P < .05, false discovery rate [FDR] < 0.20), which was confirmed to have differences in disease-specific survival (log-rank test, P = .01). This signature also significantly overlapped with published EMT cell line gene expression data (P < .05, FDR < 0.20). Finally, an EMT cell line model was established, and cells that had undergone EMT differentially expressed this signature and had significantly different EMT protein expression (P < .05, FDR < 0.20), cell migration, glucose uptake, and hexokinase activity (paired t test, P < .05). Cells that had undergone EMT also had enhanced chemotherapeutic resistance, with a higher half-maximal effective concentration than that of cells that had not undergone EMT (P < .05). Conclusion Integrative radiogenomic analysis demonstrates an association between increased normalized (18)F fluoro-2-deoxyglucose PET SUVmax, outcome, and EMT in NSCLC. (©) RSNA, 2016 Online supplemental material is available for this article.

Transcriptome profiling reveals novel gene expression signatures and regulating transcription factors of TGFβ-induced epithelial-to-mesenchymal transition.

Dysregulated epithelial to mesenchymal transition (EMT) in cancer cells endows invasive and metastatic properties upon cancer cells that favor successful colonization of distal target organs and therefore play a critical role in transforming early‐stage carcinomas into invasive malignancies. EMT has also been associated with tumor recurrence and drug resistance and cancer stem cell initiation. Therefore, better understanding of the mechanisms behind EMT could ultimately contribute to the development of novel prognostic approaches and individualized therapies that specifically target EMT processes. As an effort to characterize the central transcriptome changes during EMT, we have developed a Transforming growth factor (TGF)‐beta‐based in vitro EMT model and used it to profile EMT‐related gene transcriptional changes in two different cell lines, a non‐small cell lung cancer cell line H358, and a breast cell line MCF10a. After 7 days of TGF‐beta/Oncostatin M (OSM) treatment, changes in cell morphology to a mesenchymal phenotype were observed as well as concordant EMT‐associated changes in mRNA and protein expression. Further, increased motility was noted and flow cytometry confirmed enrichment in cancer stem cell‐like populations. Microarray‐based differential expression analysis identified an EMT‐associated gene expression signature which was confirmed by RT‐qPCR and which significantly overlapped with a previously published EMT core signature. Finally, two novel EMT‐regulating transcription factors, IRF5 and LMCD1, were identified and independently validated.

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. ©RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.

Receptor-based Surrogate Subtypes and Discrepancies with Breast Cancer Intrinsic Subtypes: Implications for Image Biomarker Development

Purpose To determine the concordance and accuracy of imaging surrogates of immunohistochemical (IHC) markers and the molecular classification of breast cancer. Materials and Methods A total of 3050 patients from 17 public breast cancer data sets containing IHC marker receptor status (estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 [HER2]) and their molecular classification (basal-like, HER2-enriched, luminal A or B) were analyzed. Diagnostic accuracy and concordance as measured with the κ statistic were calculated between the IHC and molecular classifications. Simulations were performed to assess the relationship between accuracy of imaging-based IHC markers to predict molecular classification. A simulation was performed to examine effects of misclassification of molecular type on patient survival. Results Accuracies of intrinsic subtypes based on IHC subtype were 71.7% (luminal A), 53.7% (luminal B), 64.8% (HER2-enriched), and 81.7% (basal-like). The κ agreement was fair (κ = 0.36) for luminal A and HER2-enriched subtypes, good (κ = 0.65) for the basal-like subtype, and poor (κ = 0.09) for the luminal B subtypes. Introduction of image misclassification by simulation lowered image-true subtype accuracies and κ values. Simulation analysis showed that misclassification caused survival differences between luminal A and basal-like subtypes to decrease. Conclusion There is poor concordance between triple-receptor status and intrinsic molecular subtype in breast cancer, arguing against their use in the design of prognostic genomic-based image biomarkers.

TU‐CD‐BRB‐11: A Spatiotemporal Image Phenotyping Pipeline for Radiogenomic Profiling of Breast Cancer with DCE MRI

Purpose: Objective and quantitative radiophenotyping from cancer patients is a core requirement of radiogenomic research. We present our quantitative image phenotyping pipeline which is tuned to extract spatiotemporal tumor traits from DCE breast MRI and initial results of radiogenomic association between spatiotemporal imaging features and RNA expression in breast cancer Methods: DCE MRI with 5 enhancement phases of 61 breast cancer patients were analyzed. A unified energy functional was defined for a joint shape and temporal motion estimation which incorporates intensity variation within a region along with kinetic signature. A level set evolution was carried out under the guidance of the unified energy functional to generate tumor probability maps, which is then thresholded to produce tumor masks. Among them, the observer selected the largest one. A total of 47 features were extracted from the segmented tumor comprising geometric, statistical, and spatiotemporal features. The whole procedure was carried out independently by two observers. Features from 19 cases were used as a training set to discover a prognostic imaging biomarker indicative of poor metastasis-free survival, and the remaining 39 cases were used for validation.In addition, association between the predictive imaging biomarker and genomic profiles were assessed using RNA expression data of these matched patients obtained with next-generation RNA sequencing and RT-PCR. Results: All imaging features from two independent analysis were highly consistent (r > 0.78, p<0.001). Among them, a spatiotemporal feature (enhancing rim fraction score, ERF) was shown to be strongly predictive of early metastasis (P =.009; hazard ratio,16.3; 95% confidence interval: 1.99). Radiogenomic analysis revealed five named lncRNAs significantly associated with high ERF score. Conclusion: A quantitative image phenotyping pipeline was developed which provides objective spatiotemporal features and could be successfully utilized in radiogenomic profiling of breast cancer.