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PURPOSE The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade. METHODS Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade. RESULTS Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance. CONCLUSION CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

BACKGROUND: Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. PURPOSE: To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. MATERIALS AND METHODS: In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. RESULTS: In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. CONCLUSION: Natural language processing may be applied to cancer patients’ CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021 Online supplemental material is available for this article.

BACKGROUND: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. METHODS: Patients who underwent resection of stage II/III colon cancer from 2004–2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. RESULTS: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. CONCLUSIONS: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant (p &lt, 0.001) overlap difference for spleen (DiceIML/DiceManual = 0.91/0.87), breast tumors (DiceIML/DiceManual = 0.84/0.82), and lung nodules (DiceIML/DiceManual = 0.78/0.83). For intra-rater consistency, a significant (p = 0.003) difference was found for spleen (DiceIML/DiceManual = 0.91/0.89). For inter-rater consistency, significant (p &lt, 0.045) differences were found for spleen (DiceIML/DiceManual = 0.91/0.87), breast (DiceIML/DiceManual = 0.86/0.81), lung (DiceIML/DiceManual = 0.85/0.89), the non-enhancing (DiceIML/DiceManual = 0.79/0.67) and the enhancing (DiceIML/DiceManual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin.

Summary Intrahepatic cholangiocarcinoma has known histological heterogeneity. Mutations in IDH1 (mIDH1) define a molecular subclass of intrahepatic cholangiocarcinoma and IDH-targeted therapies are in development. Characterizing mIDH1 ICC histomorphology is of clinical interest for efficient identification. Resected ICCs with targeted next-generation sequencing by MSK-IMPACT were selected. Clinical data were obtained. By slide review, blinded to IDH status, data were collected for histology type, mucin production, necrosis, fibrosis, cytoplasm cell shape (low cuboidal, plump cuboidal/polygonal, and columnar), and architectural pattern (anastomosing, tubular, compact tubular, and solid). A tumor was considered architecturally heterogeneous if no dominant pattern represented ≥75% of the tumor. Parameters were compared between mIDH1and IDH wild-type controls. In the examined cohort (113 ICC: 29 mIDH1 and 84 IDH wild-type), all IDH1-mutant tumors were of small duct–type histology, thus analysis was limited to 101 small duct–type tumors. mIDH1cases were more likely to have plump cuboidal/polygonal shape (P = .014) and geographic-type fibrosis (P = .005), while IDH1 wild-type were more likely to have low cuboidal shape (P = .005). Both groups were predominantly architecturally heterogeneous with no significant difference in the distribution of architectural patterns. Plump cuboidal/polygonal cell shape and a geographic-type pattern of intra-tumoral fibrosis are more often seen in mIDH1compared to IDH wild-type tumors; however, IDH1 mutation is not associated with a distinct histoarchitectural pattern.

PURPOSE: The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC). METHODS: In this retrospective study, consecutive patients between April 2015 and June 2016 who underwent PDAC resection were included if previously consented to a targeted sequencing protocol. Mutation status of known PDAC driver genes (KRAS, TP53, CDKN2A, and SMAD4) in the primary tumor was determined by targeted DNA sequencing and results were validated by immunohistochemistry (IHC). Radiomic features of the tumor were extracted from the preoperative CT scan and used to predict genotype and stromal content. RESULTS: The cohort for analysis consisted of 35 patients. Genomic and IHC analysis revealed alterations in KRAS in 34 (97%) patients, and changes in expression of CDKN2A in 29 (83%), SMAD4 in 16 (46%), and in TP53 in 29 (83%) patients. Models created from radiomic features demonstrated associations with SMAD4 status and the number of genes altered. The number of genes altered was the only significant predictor of overall survival (p = 0.016). By linear regression analysis, a prediction model for stromal content achieved an R(2) value of 0.731 with a root mean square error of 19.5. CONCLUSIONS: In this study, we demonstrate that in PDAC SMAD4 status and tumor stromal content can be predicted using radiomic analysis of preoperative CT imaging. These data show an association between resectable PDAC imaging features and underlying tumor biology and their potential for future precision medicine.

BACKGROUND AND AIM: Genetic alterations in intrahepatic cholangiocarcinoma (iCCA) are increasingly well characterized, but their impact on outcome and prognosis remains unknown. APPROACH AND RESULTS: This bi-institutional study of patients with confirmed iCCA (n = 412) used targeted next-generation sequencing of primary tumors to define associations among genetic alterations, clinicopathological variables, and outcome. The most common oncogenic alterations were isocitrate dehydrogenase 1 (IDH1; 20%), AT-rich interactive domain–containing protein 1A (20%), tumor protein P53 (TP53; 17%), cyclin-dependent kinase inhibitor 2A (CDKN2A; 15%), breast cancer 1–associated protein 1 (15%), FGFR2 (15%), polybromo 1 (12%), and KRAS (10%). IDH1/2 mutations (mut) were mutually exclusive with FGFR2 fusions, but neither was associated with outcome. For all patients, TP53 (P < 0.0001), KRAS (P = 0.0001), and CDKN2A (P < 0.0001) alterations predicted worse overall survival (OS). These high-risk alterations were enriched in advanced disease but adversely impacted survival across all stages, even when controlling for known correlates of outcome (multifocal disease, lymph node involvement, bile duct type, periductal infiltration). In resected patients (n = 209), TP53mut (HR, 1.82; 95% CI, 1.08–3.06; P = 0.03) and CDKN2A deletions (del; HR, 3.40; 95% CI, 1.95–5.94; P < 0.001) independently predicted shorter OS, as did high-risk clinical variables (multifocal liver disease [P < 0.001]; regional lymph node metastases [P < 0.001]), whereas KRASmut (HR, 1.69; 95% CI, 0.97–2.93; P = 0.06) trended toward statistical significance. The presence of both or neither high-risk clinical or genetic factors represented outcome extremes (median OS, 18.3 vs. 74.2 months; P < 0.001), with high-risk genetic alterations alone (median OS, 38.6 months; 95% CI, 28.8–73.5) or high-risk clinical variables alone (median OS, 37.0 months; 95% CI, 27.6-not available) associated with intermediate outcome. TP53mut, KRASmut, and CDKN2Adel similarly predicted worse outcome in patients with unresectable iCCA. CDKN2Adel tumors with high-risk clinical features were notable for limited survival and no benefit of resection over chemotherapy. CONCLUSIONS: TP53, KRAS, and CDKN2A alterations were independent prognostic factors in iCCA when controlling for clinical and pathologic variables, disease stage, and treatment. Because genetic profiling can be integrated into pretreatment therapeutic decision-making, combining clinical variables with targeted tumor sequencing may identify patient subgroups with poor outcome irrespective of treatment strategy.

PURPOSE: To evaluate the associations between computed tomography (CT) imaging features extracted from the structured American Pancreatic Association (APA)/Society of Abdominal Radiology (SAR) template and overall survival in patients with resected pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective analysis included consecutive patients with PDAC who consented to genomic tumor testing and underwent preoperative imaging and curative intent surgical resection from December 2006 to July 2017. Two radiologists assessed preoperative CT imaging using the APA/SAR PDAC reporting template. Univariable associations between overall survival and imaging variables were evaluated using Cox proportional hazards regression. RESULTS: The study included 168 patients (66 years ± 11; 91 women). 126/168 patients (75%) received upfront surgical resection whereas 42/168 (25%) received neoadjuvant therapy prior to surgical resection. In the entire cohort, features associated with decreased overall survival were tumor arterial contact of any kind (hazard ratio (HR) 1.89, 95% CI 1.13–3.14, p=0.020), tumor contact with the common hepatic artery (HR 2.33, 95% CI 1.35–4.04, p=0.009) and portal vein deformity (HR 3.22, 95% CI 1.63–6.37, p=0.003). In the upfront surgical group, larger tumor size was associated with decreased overall survival (HR 2.30, 95% CI 1.19–4.42, p=0.013). In the neoadjuvant therapy group, the presence of venous collaterals was the only feature associated with decreased overall survival (HR 2.28, 95% CI 1.04–4.99, p=0.042). CONCLUSION: The application of the APA/SAR pancreatic adenocarcinoma reporting template may identify predictors of survival that can aid in preoperative stratification of patients.

This paper contributes to the burgeoning field of surgical data science. Specifically, multi-modal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. We extracted radiomic features from CT scans and combined this with cyst-fluid markers. The cyst fluid model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Radiomic analysis of routinely acquired CT scans combined with cyst fluid inflammatory markers provides accurate prediction of risk of pancreatic cancer progression.

Purpose: For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as “debulking.” We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. Approach: This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of [Formula: see text] simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of [Formula: see text] deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. Results: In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. Conclusions: These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.

PURPOSE: To evaluate the short-term reproducibility of radiomic features in liver parenchyma and liver cancers in patients who underwent consecutive contrast-enhanced CT (CECT) with intravenous iodinated contrast within 2 weeks by chance. METHODS: The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients’ informed consent. Patients were included if they had a liver malignancy (liver metastasis, n = 22, intrahepatic cholangiocarcinoma, n = 10, and hepatocellular carcinoma, n = 6), had two consecutive CECT within 14 days, and had no prior or intervening therapy. Liver tumors and liver parenchyma were segmented and radiomic features (n = 254) were extracted. The number of reproducible features (with concordance correlation coefficients > 0.9) was calculated for patient subgroups with different variations in contrast injection rate and pixel resolution. RESULTS: The number of reproducible radiomic features decreased with increasing variations in contrast injection rate and pixel resolution. When including all CECTs with injection rates differences of less than 15% vs. up to 50%, 63/254 vs. 0/254 features were reproducible for liver parenchyma and 68/254 vs. 50/254 features were reproducible for malignancies. When including all CT with pixel resolution differences of 0–5% or 0–15%, 20/254 vs. 0/254 features were reproducible for liver parenchyma; 34/254 liver malignancy features were reproducible with pixel differences up to 15%. CONCLUSION: A greater number of liver malignancy radiomic features were reproducible compared to liver parenchyma features, but the proportion of reproducible features decreased with increasing variations in contrast injection rates and pixel resolution.

PURPOSE: Clinician gestalt may hold unexplored information that can be capitalized upon to improve existing nomograms. The study objective was to evaluate physician ability to predict 2-year overall survival (OS) in resected pancreatic ductal adenocarcinoma (PDAC) patients based on preoperative clinical characteristics and routine CT imaging. METHODS: Ten surgeons and two radiologists were provided with a clinical vignette (including age, gender, presenting symptoms, and pre-operative CA19-9 when available) and preoperative CT scan for 20 resected PDAC patients and asked to predict the probability of each patient reaching 2-year OS. Receiver operating characteristic curves were used to assess agreement and to compare performance with an established institutional nomogram. RESULTS: Ten surgeons and 2 radiologists participated in this study. The area under the curve (AUC) for all physicians was 0.707 (95%CI 0.642–0.772). Attending physicians with >5 years experience performed better than physicians with

Background Although systems of 3-dimensional image-guided surgery are a valuable adjunct across numerous procedures, differences in organ shape between that reflected in the preoperative image data and the intraoperative state can compromise the fidelity of such guidance based on the image. In this work, we assessed in real time a novel, 3-dimensional image-guided operation platform that incorporates soft tissue deformation. Methods A series of 125 alignment evaluations were performed across 20 patients. During the operation, the surgeon assessed the liver by swabbing an optically tracked stylus over the liver surface and viewing the image-guided operation display. Each patient had approximately 6 intraoperative comparative evaluations. For each assessment, 1 of only 2 types of alignments were considered: conventional rigid and novel deformable. The series of alignment types used was randomized and blinded to the surgeon. The surgeon provided a rating, R, from −3 to +3 for each display compared with the previous display, whereby a negative rating indicated degradation in fidelity and a positive rating an improvement. Results A statistical analysis of the series of rating data by the clinician indicated that the surgeons were able to perceive an improvement (defined as a R > 1) of the model-based registration over the rigid registration ( P = .01) as well as a degradation (defined as R P = .03). Conclusion This study provides evidence of the benefit of deformation correction in providing an accurate location for the liver for use in image-guided surgery systems.

PURPOSE: To evaluate the prevalence of major and ancillary imaging features from liver imaging reporting and data systems (LI-RADS) version 2014 and their interreader agreement when comparing hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (ICC) and combined tumor (cHCC-CC). METHODS: The Institutional Review Board approved this HIPAA-compliant retrospective study and waived the requirement for patients’ informed consent. Patients with resected HCC (n = 51), ICC (n = 40), and cHCC-CC (n = 11) and available pre-operative contrast-enhanced MRI were included from 2000 to 2015. Imaging features and final LI-RADS category were evaluated by four radiologists. Imaging features were compared by Fisher’s exact test and interreader agreements were assessed by κ statistics. RESULTS: None of the features were unique to either HCC or non-HCC. Imaging features that were significantly more common among HCC compared to ICC and cHCC-CC included washout (76%–78% vs. 10%–35%, p < 0.001), capsule (55%–71% vs. 16%–49%, p < 0.05), and intralesional fat (27%–52% vs. 2%–12%, p < 0.002). Features that were more common among ICC and cHCC-CC included peripheral arterial phase hyperenhancement (40%–64% vs. 10%–14%, p < 0.001) and progressive central enhancement (65%–82% vs. 14%–25%, p < 0.001). The interreader agreement was moderate for each of these imaging features (κ = 0.41–0.55). Moderate agreement was also achieved in the assignment of LR-M (κ = 0.53), with an overall sensitivity and specificity for non-HCC malignancy of 86.3% and 78.4%, respectively. CONCLUSION: HCC and non-HCC show significant differences in the prevalence of imaging features defined by LI-RADS, and are identified by radiologists with moderate interreader agreement. Using LI-RADS, radiologists also achieved moderate interreader agreement in the assignment of the LR-M category.

In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.

PURPOSE:To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. METHODS:This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II-III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images.254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. RESULTS:Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). CONCLUSIONS:Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

BACKGROUND: Colorectal liver metastases that demonstrate a complete radiographic response during chemotherapy are increasingly common with advances in chemotherapy regimens and are described as disappearing liver metastases (DLMs). However, these DLMs often continue to harbor residual viable tumor. If these tumors are found in the operating room with ultrasound (US), they should be treated. The intraoperative sonographic visualization of these lesions, however, can be hindered by chemotherapy-associated liver parenchyma changes. The objective of this study was to evaluate the use of an intraoperative image guidance system, Explorer (Analogic Corporation, Peabody, MA), to aid surgeons in the identification of DLMs initially undetected by US alone. STUDY DESIGN: In a single-arm prospective trial, patients with colorectal liver metastases undergoing liver resection and/or ablation with one or more DLMs during neoadjuvant chemotherapy were enrolled. Intraoperatively, DLMs were localized with conventional US. Any DLM not found by conventional US was re-evaluated with the image guidance system. The primary outcome was the proportion of sonographically occult DLMs subsequently located by image-guided US. RESULTS: Between April 2016 and November 2017, 25 patients with 61 DLMs were enrolled. Thirty-eight DLMs (62%) in 14 patients (56%) were not identified with US alone. Six (16%) DLMs in five patients (36%) were subsequently located with assistance of the image guidance system. The image guidance changed the intraoperative surgical plan in four of these patients. CONCLUSIONS: Image guidance can aid surgeons in the identification of initially sonographically occult DLMs and facilitate the complete surgical clearance of all sites of liver disease.

This study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. In this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs. Among pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively. Variations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions. • For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC

Summary There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.

The guest editorial introduces the Special Section on Interventional and Surgical Data Science.

Purpose: Our paper contributes to the burgeoning field of surgical data science. Specifically, multimodal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine previously defined individual models of radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. Approach: Retrospective analysis of prospectively acquired cyst fluid and CT scans was undertaken for this study. A predictive model combining clinical features with a cyst fluid inflammatory marker (CFIM) was applied to patient data. Quantitative imaging (QI) features describing radiomic patterns predictive of risk were extracted from scans. The CFIM model and QI model were combined into a single predictive model. An additional model was created with tumor-associated neutrophils (TANs) assessed by a pathologist at the time of resection. Results: Thirty-three patients were analyzed (7 high risk and 26 low risk). The CFIM model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Combining the CFIM, QI, and TAN models further increased performance to an AUC of 0.98. Conclusions: Quantitative analysis of routinely acquired CT scans combined with CFIMs provides accurate prediction of risk of pancreatic cancer progression. Although a larger cohort is needed for validation, this model represents a promising tool for preoperative assessment of IPMN.

Purpose Intraductal papillary mucinous neoplasms (IPMNs) are radiographically visible precursor lesions of pancreatic cancer. Despite standard criteria for assessing risk, only 18% of cysts are malignant at resection. Thus, a large number of patients undergo unnecessary invasive surgery for benign disease. The ability to identify IPMNs with low or high risk of transforming into invasive cancer would optimize patient selection and improve surgical decision-making. The purpose of this study was to investigate quantitative CT imaging features as markers for objective assessment of IPMN risk. Methods This retrospective study analyzed pancreatic cyst and parenchyma regions extracted from CT scans in 103 patients to predict IPMN risk. Patients who underwent resection between 2005 and 2015 with pathologically proven branch duct (BD)-IPMN and a preoperative CT scan were included in the study. Expert pathologists categorized IPMNs as low or high risk following resection as part of routine clinical care. We extracted new radiographically inspired features as well as standard texture features and designed prediction models for the categorization of high- and low-risk IPMNs. Five clinical variables were also combined with imaging features to design prediction models. Results Using images from 103 patients and tenfold cross-validation technique, the novel radiographically inspired imaging features achieved an area under the receiver operating characteristic curve (AUC) of 0.77, demonstrating their predictive power. The combination of these features with clinical variables obtained the best performance (AUC = 0.81). Conclusion The present study demonstrates that features extracted from pretreatment CT images can predict the risk of IPMN. Development of a preoperative model to discriminate between low-risk and high-risk IPMN will improve surgical decision-making.

Intrahepatic cholangiocarcinomas are histologically heterogenous. Using a cohort of 184 clinically defined, resected intrahepatic cholangiocarcinomas, we retrospectively classified the histology into 4 subtypes: large duct (LD), small duct (SD) (predominantly tubular [SD1] or predominantly anastomosing/cholangiolar, [SD2]), or indeterminate. Then, we tested the 4 subtypes for associations with risk factors, patient outcomes, histology, and immunophenotypic characteristics. SD was the most common (84%; 24% SD1 and 60% SD2) with lower proportions of LD (8%), and indeterminate (8%). Primary sclerosing cholangitis was rare (2%), but correlated with LD (P=0.005). Chronic hepatitis, frequent alcohol use, smoking, and steatosis had no histologic association. LD was associated with mucin production (P0.001), perineural invasion (P=0.002), CA19-9 staining (P0.001), CK7, CK19, CD56 immunophenotype (P=0.005), and negative albumin RNA in situ hybridization (P0.001). SD was histologically nodular (P=0.019), sclerotic (P0.001), hepatoid (P=0.042), and infiltrative at the interface with hepatocytes (P0.001). Albumin was positive in 71% of SD and 18% of LD (P=0.0021). Most albumin positive tumors (85%) lacked extracellular mucin (P0.001). S100P expression did not associate with subtype (P0.05). There was no difference in disease-specific or recurrence-free survival among the subtypes. Periductal infiltration and American Joint Committee on Cancer eighth edition pT stage predicted survival by multivariable analysis accounting for gross configuration, pT stage, and histologic type. pT2 had worse outcome relative to other pT stages. Significant differences in histology and albumin expression distinguish LD from SD, but there is insufficient evidence to support further subclassification of SD.

Soft tissue deformation can be a major source of error for image-guided interventions. Deformations associated with laparoscopic liver surgery can be substantially different from those concomitant with open approaches due to intraoperative practices such as abdominal insufflation and variable degrees of mobilization from the supporting ligaments of the liver. This technical note outlines recent contributions towards nonrigid registration for laparoscopic liver surgery published in the Journal of Medical Imaging special issue on image-guided procedures, robotic interventions, and modeling [10]. In particular, we review (1) a characterization of intraoperative liver deformation from clinically-acquired sparse digitizations of the organ surface through a series of laparoscopic-to-open conversions, and (2) a novel deformation correction strategy that leverages a set of control points placed across anatomical regions of mechanical support provided to the organ. Perturbations of these control points on a finite element model were used to iteratively reconstruct the intraoperative deformed organ shape from sparse measurements of the liver surface. These characterization and correction methods for laparoscopic deformation were applied to a retrospective clinical series of 25 laparoscopic-to-open conversions performed under image guidance and a phantom validation framework.

PURPOSE: On computed tomography (CT), intrahepatic cholangiocarcinomas (ICC) are a visibly heterogeneous group of tumors. The purpose of this study was to investigate the associations between CT imaging phenotypes, patient survival and known genetic markers. METHODS: A retrospective study was performed with 66 patients with surgically resected ICC. Pre-surgical CT images of ICC were assessed by radiologists blinded to tumor genetics and patient clinical data. Associations between qualitative imaging features and overall survival (OS) and disease free survival (DFS) were performed with Cox proportional hazards regression and visualized with Kaplan-Meier plots. Associations between radiographic features and genetic pathways (IDH1, Chromatin and RAS-MAPK) were assessed with Fisher's Exact test and the Wilcoxon Rank sum test where appropriate and corrected for multiple comparisons within each pathway using the False Discovery Rate correction. RESULTS: Three imaging features were significantly associated with a higher risk of death: necrosis (hazard ratio (HR):2.95 – 95%CI:1.44–6.04, p=0.029), satellite nodules (HR:3.29, 95% confidence interval (CI):1.35–8.02, p=0.029), and vascular encasement (HR:2.63, 95%CI:1.28–5.41, p=0.029). Additionally, with each increase in axial size, the risk of death increased (HR:1.14, 95%CI:1.03–1.26, p=0.029). Similar to findings for OS, satellite nodules (HR:3.81, 95%CI:1.88–7.71, p=0.002) and vascular encasement (HR:2.25, 95%CI:1.24–4.06, p=0.019) were associated with increased risk of recurrence/death. No significant associations were found between radiographic features and genes in the IDH1, Chromatin or RAS-MAPK pathways (p=0.63–84). CONCLUSION: This preliminary analysis of resected ICC suggests associations between CT imaging features and OS and DFS. No association was identified between imaging features and currently known genetic pathways.

Liver cancer is the second leading cause of cancer-related death worldwide.1 Hepatocellular carcinoma (HCC) is the most common primary liver cancer accounting for approximately 80% of cases. Intrahepatic cholangiocarcinoma (ICC) is a rare liver cancer, arising in patients with the same risk factors as HCC, but treatment options and prognosis differ. The diagnosis of HCC is based primarily on imaging but distinguishing between HCC and ICC is challenging due to common radiographic features.2-4 The aim of the present study is to classify HCC and ICC in portal venous phase CT. 107 patients with resected ICC and 116 patients with resected HCC were included in our analysis. We developed a deep neural network by modifying a pre-trained Inception network by retraining the final layers. The proposed method achieved the best accuracy and area under the receiver operating characteristics curve of 69.70% and 0.72, respectively on the test data.

Pancreatic neuroendocrine tumors (PanNETs) account for approximately 5% of all pancreatic tumors, affecting one individual per million each year.1 PanNETs are difficult to treat due to biological variability from benign to highly malignant, indolent to very aggressive. The World Health Organization classifies PanNETs into three categories based on cell proliferative rate, usually detected using the Ki67 index and cell morphology: low-grade (G1), intermediate-grade (G2) and high-grade (G3) tumors. Knowledge of grade prior to treatment would select patients for optimal therapy: G1/G2 tumors respond well to somatostatin analogs and targeted or cytotoxic drugs whereas G3 tumors would be targeted with platinum or alkylating agents.2, 3 Grade assessment is based on the pathologic examination of the surgical specimen, biopsy or ne-needle aspiration; however, heterogeneity in the proliferative index can lead to sampling errors.4 Based on studies relating qualitatively assessed shape and enhancement characteristics on CT imaging to tumor grade in PanNET,5 we propose objective classification of PanNET grade with quantitative analysis of CT images. Fifty-five patients were included in our retrospective analysis. A pathologist graded the tumors. Texture and shape-based features were extracted from CT. Random forest and naive Bayes classifiers were compared for the classification of G1/G2 and G3 PanNETs. The best area under the receiver operating characteristic curve (AUC) of 0:74 and accuracy of 71:64% was achieved with texture features. The shape-based features achieved an AUC of 0:70 and accuracy of 78:73%.

BACKGROUND. Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. METHODS. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19–9 levels and image features (model A), and then on CA19–9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. RESULTS. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19–9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19–9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. CONCLUSION. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Background Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort. Study Design We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [±5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression. Results From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV. Conclusions Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.

High-dimensional imaging features extracted from diagnostic imaging, called radiomics, are increasingly reported for diagnosis, prognosis, and response to therapy. Establishing the sensitivity of radiomic features to variation in scan protocols is necessary because acquisition and reconstruction parameters can vary widely across and within institutions. Our objective was to assess the reproducibility of radiomic features derived from computed tomography (CT) images by varying tube current (mA), noise index, and reconstruction [adaptive statistical iterative reconstruction (ASiR)], parameters increasingly varied by institutions seeking to reduce radiation dose in their patients. We extracted radiomic features from CT images of a uniform water phantom, anthropomorphic phantom, and a human scan. Scans were acquired from the phantoms with six tube currents (50, 100, 200, 300, 400, and 500 mA) and five noise index levels (12, 14, 16, 18, and 20), respectively. Scans of the phantoms and patient were reconstructed from 0% ASiR (i.e., filtered back projection) to 100% ASiR in increments of 10%. Two hundred and forty-eight well-known radiomic features were extracted from all scans. The concordance correlation coefficient was used to assess agreement of features. Our analysis suggests that image acquisition parameters (tube current, noise index) as well as the reconstruction technique strongly influence radiomic feature reproducibility and demonstrate a subset of reproducible features potentially usable in clinical practice.

The aim of this study was to assess splenic volume and to correlate unidimensional measurements with reference volumetric changes in chemotherapy-treated patients with colorectal cancer (CRC) liver metastases.Forty consecutive patients were selected from the cohort of a previously reported study of chemotherapy-related morbidity following major hepatectomy for CRC liver metastases. Patients were treated for 6 months prior to resection, with imaging performed at baseline and after 6 months of chemotherapy. Three unidimensional spleen measurements were recorded-width, thickness, and height (W, T, and H). Reference splenic volume was measured at baseline and after chemotherapy. The best unidimensional splenic measurement was determined by regression analysis. The 95% CI for the predicted values and R (2) values was calculated for each regression. The percentage of volume increase at 6 months was calculated.W and H showed the highest correlation with splenic volume prior to and following chemotherapy (R (2) = 0.65-0.74, p 0.001), while T showed a low correlation (R (2) = 0.11 and 0.18, p 0.05). The mean reference splenic volume increased after 6 months of chemotherapy compared to baseline (326 vs. 278 mL). Splenic volume changes showed the highest correlation with changes in W (R (2) = 0.56, p 0.001), then H (R (2) = 0.40, p 0.001), but were not significantly correlated with changes in T (R (2) = 0.01, p = 0.055).Our results show the potential utility of measuring changes in splenic width to predict clinically significant changes in splenic volume in chemotherapy-treated patients with CRC liver metastases.

In patients with colorectal cancer liver metastases (CRCLM), chemotherapy-induced hepatic injury is associated with increased splenic volume, thrombocytopenia, and decreased long-term survival. The current study investigates the relationship between change in splenic volume after preoperative chemotherapy and development of postoperative complications.The study group consisted of 80 patients who underwent resection of CRCLM; half received neoadjuvant chemotherapy for 6 months before resection (n = 40) and the other half did not (n = 40). The study group was compared with two control groups: a normal group composed of patients undergoing cholecystectomy for benign disease (n = 40) and a group of untreated, nonmetastatic colorectal cancer (CRC) patients (n = 40). Splenic volume was measured by CT/MRI volumetry. In the study group, the nontumoral liver was graded for steatosis and sinusoidal injury; operative and outcomes characteristics were also analyzed.Before chemotherapy, CRCLM patients had normalized spleen volumes of 3.2 ± 1.1 mL/kg, significantly higher than normal (2.5 ± 0.8 mL/kg; p0.001) and nonmetastatic CRC (2.6 ± 1.3 mL/kg; p0.05) patients, with higher splenic volume after 6 months of chemotherapy (4.2 ± 1.7 mL/kg; p0.01). After chemotherapy, splenic volume increase was associated with any perioperative complication (p0.01) and major complications (p0.05). Patients with ≥39% splenic volume increase (maximal chi-square test) were significantly more likely to have major complications (p0.01). Spleen volume changes were not correlated with change in platelet count (R(2) = 0.03; p = 0.301).In patients with CRCLM, the presence of liver metastases and chemotherapy are associated with higher splenic volume. Percent splenic volume increase after 6 months of chemotherapy can aid preoperative risk stratification, as it was an independent predictor of major postoperative complications.

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

Background Intraductal papillary mucinous neoplasms (IPMNs) are radiographically identifiable potential precursor lesions of pancreatic adenocarcinoma. While resection is recommended when main duct dilation is present, management of branch duct IPMN (BD-IPMN) remains controversial. This study sought to evaluate whether preoperative quantitative imaging features of BD-IPMNs could distinguish low-risk disease (low- and intermediate-grade dysplasia) from high-risk disease (high-grade dysplasia and invasive carcinoma). Methods Patients who underwent resection between 2005 and 2015 with pathologically proven BD-IPMN and a preoperative CT scan were included in the study. Quantitative image features were extracted using texture analysis and a novel quantitative mural nodularity feature developed for the study. Significant features on univariate analysis were combined with clinical variables to build a multivariate prediction model. Results Within the study group of 103 patients, 76 (74%) had low-risk disease and 27 (26%) had high-risk disease. Quantitative imaging features were prognostic of low-vs. high-risk disease. The model based only on clinical variables achieved an AUC of 0.67 and 0.79 with the addition of quantitative imaging features. Conclusion Quantitative image analysis of BD-IPMNs is a novel method that may enable risk stratification. External validation may provide a reliable non-invasive prognostic tool for clinicians.

Background Neoadjuvant treatment of colorectal liver metastases has become increasingly common, and while effective, often renders small metastases difficult to visualize on intraoperative US. The objective of this study was to determine the utility of a 3D image-guidance system in patients with intraoperative sonographically-occult CRLM. Methods 50 patients with at least one CRLM ≤ 1.5 cm were enrolled in this prospective trial of an FDA-approved Explorer image-guidance system. If the tumor(s) seen on preoperative imaging were not identified with intraoperative US, Explorer was used to target the US examination to the involved area for a more focused assessment. The primary endpoint was the proportion of cases with sonographically-occult metastases identified using Explorer. Results Forty-eight patients with preoperative scans within eight weeks of surgery were included for analysis. Forty-six patients were treated with preoperative chemotherapy (median 4 months, range 2–24 months). Overall, 22 sonographically-occult tumors in 14 patients were interrogated by Explorer, of which 15 tumors in 10 patients were located with image-guidance assistance. The only difference between patients with tumors not identified on US and those who did was the number of tumors (median 3 vs. 2, p = 0.018). Conclusion 3D image-guidance can assist in identifying small CRLM, particularly after treatment with chemotherapy. Trial registration ClinicalTrials.gov, NCT02806037 , https://clinicaltrials.gov/ct2/show/NCT02806037 .

While clinical neurosurgery has benefited from the advent of frameless image guidance for over three decades, the translation of image guided technologies to abdominal surgery, and more specifically liver resection, has been far more limited. Fundamentally, the workflow, complexity, and presentation have confounded development. With the first real efforts in translation beginning at the turn of the millennia, the work in developing novel augmented technologies to enhance screening, planning, and surgery has come to realization for the field. In this paper, we will review several examples from our own work that demonstrate the impact of image-guided procedure methods in eight clinical studies that speak to: (1) the accuracy in planning for liver resection, (2) enhanced surgical planning with portal vein embolization impact, (3) linking splenic volume changes to post-hepatectomy complications, (4) enhanced intraoperative localization in surgically occult lesions, (5) validation of deformation correction, and a (6) a novel blinded study focused at the value of deformation correction. All six of these studies were achieved in human systems and show the potential impact image guided methodologies could make on liver tissue resection procedures.

In order to rigorously validate techniques for image-guided liver surgery (IGLS), an accurate mock representation of the intraoperative surgical scene with quantifiable localization of subsurface targets would be highly desirable. However, many attempts to reproduce the laparoscopic environment have encountered limited success due to neglect of several crucial design aspects. The laparoscopic setting is complicated by factors such as gas insufflation of the abdomen, changes in patient orientation, incomplete organ mobilization from ligaments, and limited access to organ surface data. The ability to accurately represent the influences of anatomical changes and procedural limitations is critical for appropriate evaluation of IGLS methodologies such as registration and deformation correction. However, these influences have not yet been comprehensively integrated into a platform usable for assessment of methods in laparoscopic IGLS. In this work, a mock laparoscopic liver simulator was created with realistic ligamenture to emulate the complexities of this constrained surgical environment for the realization of laparoscopic IGLS. The mock surgical system reproduces an insufflated abdominal cavity with dissectible ligaments, variable levels of incline matching intraoperative patient positioning, and port locations in accordance with surgical protocol. True positions of targets embedded in a tissue-mimicking phantom are measured from CT images. Using this setup, image-to-physical registration accuracy was evaluated for simulations of laparoscopic right and left lobe mobilization to assess rigid registration performance under more realistic laparoscopic conditions. Preliminary results suggest that non-rigid organ deformations and the region of organ surface data collected affect the ability to attain highly accurate registrations in laparoscopic applications.

Hepatocellular carcinoma (HCC) is the most common liver cancer and the third leading cause of cancer-related death worldwide.1 Resection or liver transplantation may be curative in patients with early-stage HCC but early recurrence is common.2, 3 Microvascular invasion (MVI) is one of the most important predictors of early recurrence.3 The identification of MVI prior to surgery would optimally select patients for potentially curative resection or liver transplant. However, MVI can only be diagnosed by microscopic assessment of the resected tumor. The aim of the present study is to apply CT-based texture analysis to identify pre-operative imaging predictors of MVI in patients with HCC. Texture features are derived from CT and analyzed individually as well as in combination, to evaluate their ability to predict MVI. A two-stage classification is employed: HCC tumors are automatically categorized into uniform or heterogenous groups followed by classification into the presence or absence of MVI. We achieve an area under the receiver operating characteristic curve (AUC) of 0.76 and accuracy of 76.7% for uniform lesions and AUC of 0.79 and accuracy of 74.06% for heterogeneous tumors. These results suggest that MVI can be accurately and objectively predicted from preoperative CT scans.

Pancreatic cancer is the most lethal cancer with an overall 5-year survival rate of 7%1 due to the late stage at diagnosis and the ineffectiveness of current therapeutic strategies. Given the poor prognosis, early detection at a pre-cancerous stage is the best tool for preventing this disease. Intraductal papillary mucinous neoplasms (IPMN), cystic tumors of the pancreas, represent the only radiographically identifiable precursor lesion of pancreatic cancer and are known to evolve stepwise from low-to-high-grade dysplasia before progressing into an invasive carcinoma. Observation is usually recommended for low-risk (low- and intermediate-grade dysplasia) patients, while high-risk (high-grade dysplasia and invasive carcinoma) patients undergo resection; hence, patient selection is critically important in the management of pancreatic cysts.2 Radiologists use standard criteria such as main pancreatic duct size, cyst size, or presence of a solid enhancing component in the cyst to optimally select patients for surgery.3 However, these findings are subject to a radiologist’s interpretation and have been shown to be inconsistent with regards to the presence of a mural nodule or solid component.4 We propose objective classification of risk groups based on quantitative imaging features extracted from CT scans. We apply new features that represent the solid component (i.e. areas of high intensity) within the cyst and extract standard texture features. An adaptive boost classifier5 achieves the best performance with area under receiver operating characteristic curve (AUC) of 0.73 and accuracy of 77.3% for texture features. The random forest classifier achieves the best performance with AUC of 0.71 and accuracy of 70.8% with the solid component features.

Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography (CT) images.Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative CT scans were included (n = 198) in this retrospective study. Texture features extracted from the tumor and FLR, and clinicopathologic variables, were incorporated into a multivariable survival model.Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival (HDFS). Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups, with significantly different risks of hepatic recurrence (hazard ratio 2.09, 95% confidence interval 1.33-3.28; p = 0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41 vs. 20%).TA of the tumor and FLR are independently associated with OS, and TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.

Background Postoperative or remnant liver volume (RLV) after hepatic resection is a critical predictor of perioperative outcomes. This study investigates whether the accuracy of liver surgical planning software for predicting postoperative RLV and assessing early regeneration. Study Design Patients eligible for hepatic resection were approached for participation in the study from June 2008 to 2010. All patients underwent cross-sectional imaging (CT or MRI) before and early after resection. Planned remnant liver volume (pRLV) (based on the planned resection on the preoperative scan) and postoperative actual remnant liver volume (aRLV) (determined from early postoperative scan) were measured using Scout Liver software (Pathfinder Therapeutics Inc.). Differences between pRLV and aRLV were analyzed, controlling for timing of postoperative imaging. Measured total liver volume (TLV) was compared with standard equations for calculating volume. Results Sixty-six patients were enrolled in the study from June 2008 to June 2010 at 3 treatment centers. Correlation was found between pRLV and aRLV ( r = 0.941; p r = 0.953; p Conclusions Preoperative virtual planning of future liver remnant accurately predicts postoperative volume after hepatic resection. Early postoperative liver regeneration is measureable on imaging beginning at 5 days after surgery. Measuring TLV directly from CT scans rather than calculating based on equations accounts for extremes in TLV.

Acquisition of ultrasound data negatively affects image registration accuracy during image-guided therapy because of tissue compression by the probe. We present a novel compression correction method that models sub-surface tissue displacement resulting from application of a tracked probe to the tissue surface. Patient landmarks are first used to register the probe pose to pre-operative imaging. The ultrasound probe geometry is used to provide boundary conditions to a biomechanical model of the tissue. The deformation field solution of the model is inverted to non-rigidly transform the ultrasound images to an estimation of the tissue geometry before compression. Experimental results with gel phantoms indicated that the proposed method reduced the tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 1.9 ± 0.6 mm, and reduced tumor centroid alignment error from 7.6 ± 2.6 to 2.0 ± 0.9 mm. The method was applied to a clinical case and reduced the tumor margin MHD error from 5.4 ± 0.1 to 2.6 ± 0.1 mm and the centroid alignment error from 7.2 ± 0.2 to 3.5 ± 0.4 mm.

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient’s brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ∼ 11–13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future., Brain deformation during surgery compromises the fidelity of image-guided tumor resection procedures. This paper presents a comprehensive framework to intraoperatively account for volumetric brain deformations within image-guided surgery systems using only measurements of cortical surface shift. (Left) Intraoperative interface that measures the location of cortical features before and after deformation. (Right) Embedded within our correction framework is a finite element model that uses the data from our interface to constrain and estimate volumetric brain deformations.

Background: Prognostic and/or predictive biomarkers for patients with pancreatic adenocarcinoma are limited. Radiomics is a non-invasive method to quantitatively analyze tumors with imaging. Previous data have demonstrated that CT radiomics is predictive of overall survival in PDAC. Based on evidence that enhanced intratumoral adaptive immunity is associated with improved survival in PDAC, we sought to build an immune infiltration prediction model with CT radiomics. Methods: A tissue microarray (TMA) was constructed with tumor arranged in triplicate from short-term and long-term survivors of PDAC who underwent resection between 2005 and 2010. Multiplexed immunohistochemistry (IHC) was performed for intratumoral mature dendritic cells (DC-LAMP+), regulatory T cells (CD3+FoxP3+), CD8+ T cells, (CD3+CD8+), activated cytolytic CD8+ T cells (CD3+CD8+GrB+), and macrophages (CD68+). The pancreatic tumor in the portal venous phase on preoperative CT was manually delineated, and 255 radiomic features were extracted and analyzed for significance using univariate linear regression. A multivariate linear regression analysis was then performed to correlate radiomic features with immune infiltrate for each of the immune phenotypes. Results: There were 22 patients included in the analysis, 7 of whom were short-term survivors (median survival of 9 months) and 15 were long-term survivors (median survival of 74 months). Feature selection resulted in 17 significant radiomic features for mature dendritic cells, 7 for regulatory T cells, 11 for CD8+ T cells, 16 for cytolytic CD8+ T cells and 12 features for macrophages. Five regression models were then constructed using the significant radiomic features to predict intratumoral immune infiltrate. There was a strong association between radiomic features and mature dendritic cells (R2=0.83, 95% CI 0.761-0.906, p Conclusion: A strong association was found between imaging phenotypes and both dendritic cells and activated cytolytic T cells, demonstrating that non-invasive imaging techniques may help identify tumors with active adaptive immunity. Further analysis is underway in a larger cohort of patients to validate these findings. Citation Format: Caitlin A. McIntyre, Jayasree Chakraborty, Maura Koszalka, Jared Bassmann, Yuting Chou, Mithat Gonen, Peter J. Allen, T. Peter Kingham, Michael I. D'Angelica, Jeffrey A. Drebin, William R. Jarnagin, Richard K. Do, Vinod P. Balachandran, Amber L. Simpson. The use of CT radiomics to predict immune infiltrate in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2444.