Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction.

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes, especially as the disease progresses into liver metastases. Computed tomography (CT) is a frontline tool for this task; however, the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm. We hypothesized that image reconstruction with a high-frequency kernel could result in a better characterization of liver metastases features via deep neural networks. This kernel produces images that appear noisier but preserve more sinogram information. A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases. This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases, and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim. Datasets of 10,000 liver metastases were generated, scanned, and reconstructed using either standard or high-frequency kernels. These data were used to train and validate deep neural networks to recover crafted metastases characteristics, such as internal heterogeneity, edge sharpness, and edge fractal dimension. In the absence of noise, models scored, on average, 12.2% ( α = 0.012 ) and 7.5% ( α = 0.049 ) lower squared error for characterizing edge sharpness and fractal dimension, respectively, when using high-frequency reconstructions compared to standard. However, the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan. Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization, provided that noise is limited. Future work should investigate the information-preserving kernels in datasets with clinical labels.

Clinicogenomic predictors of outcomes in patients with hepatocellular carcinoma treated with immunotherapy.

INTRODUCTION: Immune checkpoint inhibitor (ICI) combinations extend overall survival (OS) while anti-PD-1/L1 monotherapy is non-inferior to sorafenib in treatment-naïve, patients with advanced hepatocellular carcinoma (HCC). Clinicogenomic features are posited to influence patient outcomes. METHODS: The primary objective of this retrospective study was to define the clinical, pathologic, and genomic factors associated with outcomes to ICI therapy in patients with HCC. Patients with histologically confirmed advanced HCC treated with ICI at Memorial Sloan Kettering Cancer Center from 2012 to 2022 were included. Association between clinical, pathological, and genomic characteristics were assessed with univariable and multivariable Cox regression model for progression-free survival (PFS) and OS. RESULTS: Two-hundred and forty-two patients were treated with ICI-based therapy. Patients were predominantly male (82%) with virally mediated HCC (53%) and Child Pugh A score (70%). Median follow-up was 28 months (0.5-78.4). Median PFS for those treated in 1st line, 2nd line and ≥ 3rd line was 4.9 (range: 2.9-6.2), 3.1 (2.3-4.0), and 2.5 (2.1-4.0) months, respectively. Median OS for those treated in 1st line, 2nd line, and ≥ 3rd line was 16 (11-22), 7.5 (6.4-11), and 6.4 (4.6-26) months, respectively. Poor liver function and performance status associated with worse PFS and OS, while viral hepatitis C was associated with favorable outcome. Genetic alterations were not associated with outcomes. CONCLUSION: Clinicopathologic factors were the major determinates of outcomes for patients with advanced HCC treated with ICI. Molecular profiling did not aid in stratification of ICI outcomes. Future studies should explore alternative biomarkers such as the level of immune activation or the pretreatment composition of the immune tumor microenvironment.

Vertebrates on a Chip: Noninvasive Electrical and Optical Mapping of Whole Brain Activity Associated with Pharmacological Treatments.

Mapping brain activities is necessary for understanding brain physiology and discovering new treatments for neurological disorders. Such efforts have greatly benefited from the advancement in technologies for analyzing neural activity with improving temporal or spatial resolution. Here, we constructed a multielectrode array based brain activity mapping (BAM) system capable of stabilizing and orienting zebrafish larvae for recording electroencephalogram (EEG) like local field potential (LFP) signals and brain-wide calcium dynamics in awake zebrafish. Particularly, we designed a zebrafish trap chip that integrates with an eight-by-eight surface electrode array, so that brain electrophysiology can be noninvasively recorded in an agarose-free and anesthetic-free format with a high temporal resolution of 40 µs, matching the capability typically achieved by invasive LFP recording. Benefiting from the specially designed hybrid system, we can also conduct calcium imaging directly on immobilized awake larval zebrafish, which further supplies us with high spatial resolution brain-wide activity data. All of these innovations reconcile the limitations of sole LFP recording or calcium imaging, emphasizing a synergy of combining electrical and optical modalities within one unified device for activity mapping across a whole vertebrate brain with both improved spatial and temporal resolutions. The compatibility with in vivo drug treatment further makes it suitable for pharmacology studies based on multimodal measurement of brain-wide physiology.

Distinct CT imaging features of new liver metastases from primary genitourinary cancers.

PURPOSE: To apply natural language processing (NLP) to a large volume of structured radiology reports in the investigation of CT imaging features of new liver metastases from primary genitourinary cancers. METHODS: In this retrospective study, a previously reported NLP model was applied to consecutive structured CT reports from 2016 to 2022 to predict those patients with primary genitourinary cancer who developed liver metastasis. Pathology or imaging follow-up served as the reference standard for validating NLP predictions. Subsequently, diagnostic CTs of the identified patients were qualitatively assessed by two radiologists, whereby several imaging features of new liver metastasis were assessed. Proportions of the assessed imaging features were compared between primary genitourinary cancers using the Chi-square or Fisher's exact test. RESULTS: In 112 patients (mean age = 72 years; 83 males), the majority of new liver metastases were hypovascular (73.2%), well defined (76.6%), homogenous (66.9%), and without necrotic/cystic component (73.2%). There was a higher proportion of iso- to hyperdense liver metastases for primary kidney cancer vs other primary genitourinary cancers (42.5% in kidney cancer; 2.3% in ureter/bladder cancer, 8% in prostate cancer, and 0% in testicular cancer; p < 0.05) and a higher proportion of new liver metastases with ill-defined margin for primary prostate cancer vs other primary genitourinary cancers (44.0% in prostate cancer, 15.0% in kidney cancer, 18.6% in ureter/bladder cancer, and 25.0% in testicular cancer; p < 0.05). CONCLUSION: New liver metastases from primary genitourinary cancers tend to be hypovascular and show several distinct imaging features between different primary genitourinary cancers.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group.

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis.

PURPOSE: To identify significant MRI features associated with macrotrabecular-massive hepatocellular carcinoma (MTM-HCC), and to assess the distribution of Liver Imaging Radiology and Data System (LI-RADS, LR) category assignments. METHODS: PubMed and EMBASE were searched up to March 28, 2023. Random-effects model was constructed to calculate pooled diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) for each MRI feature for differentiating MTM-HCC from NMTM-HCC. The pooled proportions of LI-RADS category assignments in MTM-HCC and NMTM-HCC were compared using z-test. RESULTS: Ten studies included 1978 patients with 2031 HCCs (426 (20.9%) MTM-HCC and 1605 (79.1%) NMTM-HCC). Six MRI features showed significant association with MTM-HCC: tumor in vein (TIV) (DOR = 2.4 [95% CI, 1.6-3.5]), rim arterial phase hyperenhancement (DOR =2.6 [95% CI, 1.4-5.0]), corona enhancement (DOR = 2.6 [95% CI, 1.4-4.5]), intratumoral arteries (DOR = 2.6 [95% CI, 1.1-6.3]), peritumoral hypointensity on hepatobiliary phase (DOR = 2.2 [95% CI, 1.5-3.3]), and necrosis (DOR = 4.2 [95% CI, 2.0-8.5]). The pooled proportions of LI-RADS categories in MTM-HCC were LR-3, 0% [95% CI, 0-2%]; LR-4, 11% [95% CI, 6-16%]; LR-5, 63% [95% CI, 55-71%]; LR-M, 12% [95% CI, 6-19%]; and LR-TIV, 13% [95% CI, 6-22%]. In NMTM-HCC, the pooled proportions of LI-RADS categories were LR-3, 1% [95% CI, 0-2%]; LR-4, 8% [95% CI, 3-15%]; LR-5, 77% [95% CI, 71-82%]; LR-M, 5% [95% CI, 3-7%]; and LR-TIV, 6% [95% CI, 2-11%]. MTM-HCC had significantly lower proportion of LR-5 and higher proportion of LR-M and LR-TIV categories. CONCLUSIONS: Six MRI features showed significant association with MTM-HCC. Additionally, compared to NMTM-HCC, MTM-HCC are more likely to be categorized LR-M and LR-TIV and less likely to be categorized LR-5. CLINICAL RELEVANCE STATEMENT: Several MR imaging features can suggest macrotrabecular-massive hepatocellular carcinoma subtype, which can assist in guiding treatment plans and identifying potential candidates for clinical trials of new treatment strategies. KEY POINTS: • Macrotrabecular-massive hepatocellular carcinoma is a subtype of HCC characterized by its aggressive nature and unfavorable prognosis. • Tumor in vein, rim arterial phase hyperenhancement, corona enhancement, intratumoral arteries, peritumoral hypointensity on hepatobiliary phase, and necrosis on MRI are indicative of macrotrabecular-massive hepatocellular carcinoma. • Various MRI characteristics can be utilized for the diagnosis of the macrotrabecular-massive hepatocellular carcinoma subtype. This can prove beneficial in guiding treatment decisions and identifying potential candidates for clinical trials involving novel treatment approaches.

Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon.

Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the utility of applying natural language processing (NLP) to the large-scale interpretation of disease responses using a standardized oncologic response lexicon (OR-RADS) to facilitate RWE collection. Radiologists annotated 3503 retrospectively collected clinical impressions from radiological reports across several cancer types with one of seven OR-RADS categories. A Bidirectional Encoder Representations from Transformers (BERT) model was trained on this dataset with an 80-20% train/test split to perform multiclass and single-class classification tasks using the OR-RADS. Radiologists also performed the classification to compare human and model performance. The model achieved accuracies from 95 to 99% across all classification tasks, performing better in single-class tasks compared to the multiclass task and producing minimal misclassifications, which pertained mostly to overpredicting the equivocal and mixed OR-RADS labels. Human accuracy ranged from 74 to 93% across all classification tasks, performing better on single-class tasks. This study demonstrates the feasibility of the BERT NLP model in predicting disease response in cancer patients, exceeding human performance, and encourages the use of the standardized OR-RADS lexicon to improve large-scale prediction accuracy.

Targeted Molecular Profiling of Circulating Cell-Free DNA in Patients With Advanced Hepatocellular Carcinoma.

PURPOSE: Next-generation sequencing (NGS) of tumor-derived, circulating cell-free DNA (cfDNA) may aid in diagnosis, prognostication, and treatment of patients with hepatocellular carcinoma (HCC). The operating characteristics of cfDNA mutational profiling must be determined before routine clinical implementation. METHODS: This was a single-center, retrospective study with the primary objective of defining genomic alterations in circulating cfDNA along with plasma-tissue genotype agreement between NGS of matched tumor samples in patients with advanced HCC. cfDNA was analyzed using a clinically validated 129-gene NGS assay; matched tissue-based NGS was analyzed with a US Food and Drug Administration-authorized NGS tumor assay. RESULTS: Fifty-three plasma samples from 51 patients with histologically confirmed HCC underwent NGS-based cfDNA analysis. Genomic alterations were detected in 92.2% of patients, with the most commonly mutated genes including TERT promoter (57%), TP53 (47%), CTNNB1 (37%), ARID1A (18%), and TSC2 (14%). In total, 37 (73%) patients underwent paired tumor NGS, and concordance was high for mutations observed in patient-matched plasma samples: TERT (83%), TP53 (94%), CTNNB1 (92%), ARID1A (100%), and TSC2 (71%). In 10 (27%) of 37 tumor-plasma samples, alterations were detected by cfDNA analysis that were not detected in the patient-matched tumors. Potentially actionable mutations were identified in 37% of all cases including oncogenic/likely oncogenic alterations in TSC1/2 (18%), BRCA1/2 (8%), and PIK3CA (8%). Higher average variant allele fraction was associated with elevated alpha-fetoprotein, increased tumor volume, and no previous systemic therapy, but did not correlate with overall survival in treatment-naïve patients. CONCLUSION: Tumor mutation profiling of cfDNA in HCC represents an alternative to tissue-based genomic profiling, given the high degree of tumor-plasma NGS concordance; however, genotyping of both blood and tumor may be required to detect all clinically actionable genomic alterations.

Determinants of Survival with Combined HER2 and PD-1 Blockade in Metastatic Esophagogastric Cancer.

PURPOSE: We report updated clinical outcomes from a phase II study of pembrolizumab, trastuzumab, and chemotherapy (PTC) in metastatic esophagogastric cancer in conjunction with outcomes from an independent Memorial Sloan Kettering (MSK) cohort. PATIENTS AND METHODS: The significance of pretreatment 89Zr-trastuzumab PET, plasma circulating tumor DNA (ctDNA) dynamics, and tumor HER2 expression and whole exome sequencing was evaluated to identify prognostic biomarkers and mechanisms of resistance in patients treated on-protocol with PTC. Additional prognostic features were evaluated using a multivariable Cox regression model of trastuzumab-treated MSK patients (n = 226). Single-cell RNA sequencing (scRNA-seq) data from MSK and Samsung were evaluated for mechanisms of therapy resistance. RESULTS: 89Zr-trastuzumab PET, scRNA-seq, and serial ctDNA with CT imaging identified how pre-treatment intrapatient genomic heterogeneity contributes to inferior progression-free survival (PFS). We demonstrated that the presence of intensely avid lesions by 89Zr-trastuzumab PET declines in tumor-matched ctDNA by 3 weeks, and clearance of tumor-matched ctDNA by 9 weeks were minimally invasive biomarkers of durable PFS. Paired pre- and on-treatment scRNA-seq identified rapid clearance of HER2-expressing tumor clones with expansion of clones expressing a transcriptional resistance program, which was associated with MT1H, MT1E, MT2A, and MSMB expression. Among trastuzumab-treated patients at MSK, ERBB2 amplification was associated with improved PFS, while alterations in MYC and CDKN2A/B were associated with inferior PFS. CONCLUSIONS: These findings highlight the clinical relevance of identifying baseline intrapatient heterogeneity and serial ctDNA monitoring of HER2-positive esophagogastric cancer patients to identify early evidence of treatment resistance, which could guide proactive therapy escalation or deescalation.

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging. Approach: Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators. Results: Average target registration errors were 2.56±2.45 mm for a biomechanical image registration algorithm and 4.15±3.63 mm for a diffeomorphic intensity-based algorithm, corresponding to 1-2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators. Conclusions: Volumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is lethal in 88% of patients1, yet harbours mutation-derived T cell neoantigens that are suitable for vaccines 2,3. Here in a phase I trial of adjuvant autogene cevumeran, an individualized neoantigen vaccine based on uridine mRNA-lipoplex nanoparticles, we synthesized mRNA neoantigen vaccines in real time from surgically resected PDAC tumours. After surgery, we sequentially administered atezolizumab (an anti-PD-L1 immunotherapy), autogene cevumeran (a maximum of 20 neoantigens per patient) and a modified version of a four-drug chemotherapy regimen (mFOLFIRINOX, comprising folinic acid, fluorouracil, irinotecan and oxaliplatin). The end points included vaccine-induced neoantigen-specific T cells by high-threshold assays, 18-month recurrence-free survival and oncologic feasibility. We treated 16 patients with atezolizumab and autogene cevumeran, then 15 patients with mFOLFIRINOX. Autogene cevumeran was administered within 3 days of benchmarked times, was tolerable and induced de novo high-magnitude neoantigen-specific T cells in 8 out of 16 patients, with half targeting more than one vaccine neoantigen. Using a new mathematical strategy to track T cell clones (CloneTrack) and functional assays, we found that vaccine-expanded T cells comprised up to 10% of all blood T cells, re-expanded with a vaccine booster and included long-lived polyfunctional neoantigen-specific effector CD8+ T cells. At 18-month median follow-up, patients with vaccine-expanded T cells (responders) had a longer median recurrence-free survival (not reached) compared with patients without vaccine-expanded T cells (non-responders; 13.4 months, P = 0.003). Differences in the immune fitness of the patients did not confound this correlation, as responders and non-responders mounted equivalent immunity to a concurrent unrelated mRNA vaccine against SARS-CoV-2. Thus, adjuvant atezolizumab, autogene cevumeran and mFOLFIRINOX induces substantial T cell activity that may correlate with delayed PDAC recurrence.

Artificial Intelligence in CT and MR Imaging for Oncological Applications.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.

The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively.

Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI.

As the incidence of hepatocellular carcinoma (HCC) and subsequent treatments with liver-directed therapies rise, the complexity of assessing lesion response has also increased. The Liver Imaging Reporting and Data Systems (LI-RADS) treatment response algorithm (LI-RADS TRA) was created to standardize the assessment of response after locoregional therapy (LRT) on contrast-enhanced CT or MRI. Originally created based on expert opinion, these guidelines are currently undergoing revision based on emerging evidence. While many studies support the use of LR-TRA for evaluation of HCC response after thermal ablation and intra-arterial embolic therapy, data suggest a need for refinements to improve assessment after radiation therapy. In this manuscript, we review expected MR imaging findings after different forms of LRT, clarify how to apply the current LI-RADS TRA by type of LRT, explore emerging literature on LI-RADS TRA, and highlight future updates to the algorithm. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Phase 1 Dose Escalation Study of SBRT Using 3 Fractions for Locally Advanced Pancreatic Cancer.

PURPOSE: The optimal dose and fractionation of stereotactic body radiation therapy (SBRT) for locally advanced pancreatic cancer (LAPC) have not been defined. Single-fraction SBRT was associated with more gastrointestinal toxicity, so 5-fraction regimens have become more commonly employed. We aimed to determine the safety and maximally tolerated dose of 3-fraction SBRT for LAPC. METHODS AND MATERIALS: Two parallel phase 1 dose escalation trials were conducted from 2016 to 2019 at Memorial Sloan Kettering Cancer Center and University of Colorado. Patients with histologically confirmed LAPC without distant progression after at least 2 months of induction chemotherapy were eligible. Patients received 3-fraction linear accelerator-based SBRT at 3 dose levels, 27, 30, and 33 Gy, following a modified 3+3 design. Dose-limiting toxicity, defined as grade ≥3 gastrointestinal toxicity within 90 days, was scored by National Cancer Institute Common Terminology Criteria for Adverse Events, version 4. The secondary endpoints included cumulative incidence of local failure (LF) and distant metastasis (DM), as well as progression-free and overall survival PFS and OS, respectively, toxicity, and quality of life (QoL) using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30) and the pancreatic cancer-specific QLQ-PAN26 questionnaire. RESULTS: Twenty-four consecutive patients were enrolled (27 Gy: 9, 30 Gy: 8, 33 Gy: 7). The median (range) age was 67 (52-79) years, and 12 (50%) had a head/uncinate tumor location, with a median tumor size of 3.8 (1.1-11) cm and CA19-9 of 60 (1-4880) U/mL. All received chemotherapy for a median of 4 (1.4-10) months. There were no grade ≥3 toxicities. Two-year rates (95% confidence interval) of LF, DM, PFS, and OS were 31.7% (8.6%-54.8%), 70.2% (49.7%-90.8%), 20.8% (4.6%-37.1%), and 29.2% (11.0%-47.4%), respectively. Three- and 6-month QoL assessment showed no detriment. CONCLUSIONS: For select patients with LAPC, dose escalation to 33 Gy in 3 fractions resulted in no dose-limiting toxicities, no detriments to QoL, and disease outcomes comparable with conventional RT. Further exploration of SBRT schemes to maximize tumor control while enabling efficient integration with systemic therapy is warranted.

LI-RADS: Looking Back, Looking Forward.

Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.

Interim Analysis of a Prospective Validation of 2 Blood-Based Genomic Assessments (PPQ and NETest) to Determine the Clinical Efficacy of 177Lu-DOTATATE in Neuroendocrine Tumors.

Reliable biomarkers for neuroendocrine tumor (NET) management during peptide receptor radionuclide therapy (PRRT) are lacking. We validated the role of 2 circulating biomarkers: the PRRT prediction quotient (PPQ) as a predictive marker for response and the NETest as a monitoring biomarker. Furthermore, we evaluated whether tissue-based genetic alterations are effective in predicting progression-free survival (PFS). Methods: Data were prospectively collected on patients at the Memorial Sloan Kettering Cancer Center with 177Lu-DOTATATE-treated somatostatin receptor (SSTR)-positive gastroenteropancreatic and lung NETs (n = 67; median age, 66 y; 52% female; 42% pancreatic, 39% small-bowel; 78% grade 1 or 2). All cases were metastatic (89% liver) and had received 1-8 prior treatments (median, 3), including somatostatin analogs (91%), surgery (55%), or chemotherapy (49%). Treatment response included PFS. According to RECIST, version 1.1, responders had stable disease or a partial response (disease-control rate) and nonresponders had progression. Blood was collected before each cycle and at follow-up. Samples were deidentified and assayed and underwent masked analyses. The gene expression assays included RNA isolation, real-time quantitative polymerase chain reaction, and multialgorithm analyses. The PPQ (positive predicts a responder; negative predicts a nonresponder) at baseline was determined. The NETest (0-100 score) was performed. Statistics were analyzed using Mann-Whitney U testing (2-tailed) or Kaplan-Meier survival testing (PFS). In patients with archival tumor tissue, next-generation sequencing was performed through an institutional platform (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets). Results: Forty-one patients (61%) were responders. PPQ accurately predicted 96% (64/67). The hazard ratio for prediction was 24.4 (95% CI, 8.2-72.5). Twelve-month disease control was 97% for PPQ-positive patients versus 26% for PPQ-negative patients (P < 0.0001). Median progression-free survival was not reached in those predicted to respond (PPQ-positive, n = 40) but was 8 mo in those predicted not to respond (PPQ-negative, n = 27). The NETest result in responders was 67 ± 25 at baseline and significantly (P < 0.05) decreased (-37 ± 44%) at follow-up. The NETest result in nonresponders was 44 ± 23 at baseline and significantly (P < 0.05) increased (+76% ± 56%) at progression. Overall, the NETest changes (increases or decreases) were 90% accurate. Thirty patients underwent next-generation sequencing. Tumors were microsatellite-stable, and the median mutational burden was 1.8. Alterations involved mainly the mTOR/PTEN/TSC pathway (30%). No relationship was associated with PRRT response. Conclusion: Our interim analysis confirmed that PPQ is an accurate predictor of 177Lu-DOTATATE responsiveness (radiosensitivity) and that NETest changes accurately correlated with treatment response. Tissue-based molecular genetic information had little value in PRRT prediction. Blood-based gene signatures may improve the management of patients undergoing 177Lu-DOTATATE by providing information on tumor radiosensitivity and disease course, thus allowing individualized strategies.