A Multicenter Study of Needle Size and Safety for Splenic Biopsy.

Background Splenic biopsy is rarely performed because of the perceived risk of hemorrhagic complications. Purpose To evaluate the safety of large bore (≥18 gauge) image-guided splenic biopsy. Materials and Methods This retrospective study included consecutive adult patients who underwent US- or CT-guided splenic biopsy between March 2001 and March 2022 at eight academic institutions in the United States. Biopsies were performed with needles that were 18 gauge or larger, with a comparison group of biopsies with needles smaller than 18 gauge. The primary outcome was significant bleeding after the procedure, defined by the presence of bleeding at CT performed within 30 days or angiography and/or surgery performed to manage the bleeding. Categorical variables were compared using the χ2 test and medians were compared using the Mann-Whitney test. Results A total of 239 patients (median age, 63 years; IQR, 50-71 years; 116 of 239 [48.5%] female patients) underwent splenic biopsy with an 18-gauge or smaller needle and 139 patients (median age, 58 years [IQR, 49-69 years]; 66 of 139 [47.5%] female patients) underwent biopsy with a needle larger than 18 gauge. Bleeding was detected in 20 of 239 (8.4%) patients in the 18-gauge or smaller group and 11 of 139 (7.9%) in the larger than 18-gauge group. Bleeding was treated in five of 239 (2.1%) patients in the 18-gauge or smaller group and one of 139 (1%) in the larger than 18-gauge group. No deaths related to the biopsy procedure were recorded during the study period. Patients with bleeding after biopsy had smaller lesions compared with patients without bleeding (median, 2.1 cm [IQR, 1.6-5.4 cm] vs 3.5 cm [IQR, 2-6.8 cm], respectively; P = .03). Patients with a history of lymphoma or leukemia showed a lower incidence of bleeding than patients without this history (three of 90 [3%] vs 28 of 288 [9.7%], respectively; P = .05). Conclusion Bleeding after splenic biopsy with a needle 18 gauge or larger was similar to biopsy with a needle smaller than 18 gauge and seen in 8% of procedures overall, with 2% overall requiring treatment. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Grant in this issue.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. RESULTS. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Subtype-specific characterization of breast cancer invasion using a microfluidic tumor platform.

Understanding progression of breast cancers to invasive ductal carcinoma (IDC) can significantly improve breast cancer treatments. However, it is still difficult to identify genetic signatures and the role of tumor microenvironment to distinguish pathological stages of pre-invasive lesion and IDC. Presence of multiple subtypes of breast cancers makes the assessment more challenging. In this study, an in-vitro microfluidic assay was developed to quantitatively assess the subtype-specific invasion potential of breast cancers. The developed assay is a microfluidic platform in which a ductal structure of epithelial cancer cells is surrounded with a three-dimensional (3D) collagen matrix. In the developed platform, two triple negative cancer subtypes (MDA-MB-231 and SUM-159PT) invaded into the surrounding matrix but the luminal A subtype, MCF-7, did not. Among invasive subtypes, SUM-159PT cells showed significantly higher invasion and degradation of the surrounding matrix than MDA-MB-231. Interestingly, the cells cultured on the platform expressed higher levels of CD24 than in their conventional 2D cultures. This microfluidic platform may be a useful tool to characterize and predict invasive potential of breast cancer subtypes or patient-derived cells.

Differential response to doxorubicin in breast cancer subtypes simulated by a microfluidic tumor model.

Successful drug delivery and overcoming drug resistance are the primary clinical challenges for management and treatment of cancer. The ability to rapidly screen drugs and delivery systems within physiologically relevant environments is critically important; yet is currently limited due to lack of appropriate tumor models. To address this problem, we developed the Tumor-microenvironment-on-chip (T-MOC), a new microfluidic tumor model simulating the interstitial flow, plasma clearance, and transport of the drug within the tumor. We demonstrated T-MOC's capabilities by assessing the delivery and efficacy of doxorubicin in small molecular form versus hyaluronic acid nanoparticle (NP) formulation in MCF-7 and MDA-MB-231, two cell lines representative of different molecular subtypes of breast cancer. Doxorubicin accumulated and penetrated similarly in both cell lines while the NP accumulated more in MDA-MB-231 than MCF-7 potentially due to binding of hyaluronic acid to CD44 expressed by MDA-MB-231. However, the penetration of the NP was less than the molecular drug due to its larger size. In addition, both cell lines cultured on the T-MOC showed increased resistance to the drug compared to 2D culture where MDA-MB-231 attained a drug-resistant tumor-initiating phenotype indicated by increased CD44 expression. When grown in immunocompromised mice, both cell lines exhibited cell-type-dependent resistance and phenotypic changes similar to T-MOC, confirming its predictive ability for in vivo drug response. This initial characterization of T-MOC indicates its transformative potential for in vitro testing of drug efficacy towards prediction of in vivo outcomes and investigation of drug resistance mechanisms for advancement of personalized medicine.