Background The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making. Methods Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. Results A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66. Conclusions Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman’s rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

Background and purpose: Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes. Materials and methods: 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation. Clinical contours of primary tumours were not recontoured and were used for Pyradiomics based feature extraction. ComBat harmonization was applied, and LASSO-Logistic Regression (LR) and Support Vector Machine (SVM) models were built. 95% confidence interval (CI) of 1000 bootstrapped area-under-the-Receiver-operating-curves (AUC) provided predictive performance. Responsiveness of the models’ performance to the choice of feature selection methods, ComBat harmonization, machine learning classifier, single and pooled data was evaluated. Results: LASSO and SelectKBest selected 14 and 16 features, respectively; three were overlapping. Without ComBat, the LR and SVM models for three institutional data showed AUCs (CI) of 0.513 (0.481–0.559) and 0.632 (0.586–0.665), respectively. Performances following ComBat revealed AUCs of 0.559 (0.536–0.590) and 0.662 (0.606–0.690), respectively. Compared to single cohort AUCs (0.562–0.629), SVM models from pooled data performed significantly better at AUC = 0.680. Conclusions: Multi-institutional retrospective data accentuates the existing variabilities that affect radiomics. Carefully designed prospective, multi-institutional studies and data sharing are necessary for clinically relevant head-and-neck cancer prognostication models.

Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). Aims: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. Settings and Design: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. Subjects and Methods: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). Results: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51–0.54). Conclusions: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

Background: Thoracic spine has complex pedicle anatomy with a narrow canal diameter which makes pedicle screw insertion challenging. Fennell et al. have described a simple freehand technique of thoracic pedicle screw placement. We have tested the accuracy of Fennell technique using computed tomography-based (CT-based) simulation model with pedicle screw simulator (PSS). Methods: Normal CT thoracic spine obtained from CT thorax data of five patients were used in the 3D slicer environment using PSS for simulation. Entry points and axial trajectory as described by Fennell et al. and a sagittal trajectory parallel to the superior endplate were used for simulating the freehand technique using EA (entry angle) mode in the PSS. An ideal trajectory through the midsection of the pedicle from the same entry point and a sagittal trajectory parallel to the superior endplate were simulated using the ET (Entry Target) mode. Angle predicted by the software for an ideal axial trajectory was compared with the Fennell technique and this angle difference was noted at all the levels. Presence of pedicle breach was noted while simulating the Fennell technique. Results: A total of 240 thoracic pedicle screw insertions were simulated, 120 screws by each technique. A sagittal trajectory parallel to the superior endplate caused no pedicle breach in the cranial-caudal direction at any level. No medial or lateral breach was noted while using an axial trajectory of 30 degrees at T1-T2 and 20 degrees from T3-T10. A 20 degrees axial trajectory at T11 and T12 resulted in a breach of the medial cortex and the ideal mean axial angles at T11 and T12 were 2.8 degrees and 6.5 degrees, respectively. Conclusions: Fennell technique was effectively simulated using PSS. A uniform entry point and sagittal trajectory parallel to the superior endplate serves as a useful guide for freehand insertion of thoracic pedicle screws. At T1 1 and 12, ideal axial trajectories are less than 10 degrees.

Background: Lumbar pedicle screw insertion involves a steep learning curve for novice spine surgeons and requires image guidance or navigation. Small volume centers may be handicapped by the lack of cost-effective user-friendly tools for preoperative planning, guidance, and decision making. Objective: We describe a patient-specific interactive software module, pedicle screw simulator (PSS), for virtual preoperative planning to determine the entry point and visualize the trajectories of pedicle screws. Materials and Methods: The PSS was coded in Python for use in an open source image processing software, 3D Slicer. Preoperative computed tomography (CT) data of each subject was loaded into this module. The entry-target (ET) mode calculates the ideal angle from the entry point through the widest section of the pedicle to the desired target in the vertebral body. The entry-angle (EA) mode projects the screw trajectory from the desired entry point at a desired angle. The performance of this software was tested using CT data from four subjects. Results: PSS provided a quantitative and qualitative feedback preoperatively to the surgeon about the entry point and trajectories of pedicle screws. It also enabled the surgeons to visualize and predict the pedicle breach with various trajectories. Conclusion: This interactive software module aids in understanding and correcting the orientation of each vertebra in three-dimensions, to identify the ideal entry points, angles of insertion and trajectories for pedicle screw insertion to suit the local anatomy.

This paper presents an improved GrowCut (IGC), a positron emission tomography-based segmentation algorithm, and tests its clinical applicability. Contrary to the traditional method that requires the user to provide the initial seeds, the IGC algorithm starts with a threshold-based estimate of the tumor and a three-dimensional morphologically grown shell around the tumor as the foreground and background seeds, respectively. The repeatability of IGC from the same observer at multiple time points was compared with the traditional GrowCut algorithm. The algorithm was tested in 11 nonsmall cell lung cancer lesions and validated against the clinician-defined manual contour and compared against the clinically used 25% of the maximum standardized uptake value [SUV-(max)], 40% [Formula: see text], and adaptive threshold methods. The time to edit IGC-defined functional volume to arrive at the gross tumor volume (GTV) was compared with that of manual contouring. The repeatability of the IGC algorithm was very high compared with the traditional GrowCut ([Formula: see text]) and demonstrated higher agreement with the manual contour with respect to threshold-based methods. Compared with manual contouring, editing the IGC achieved the GTV in significantly less time ([Formula: see text]). The IGC algorithm offers a highly repeatable functional volume and serves as an effective initial guess that can well minimize the time spent on labor-intensive manual contouring.

In this study we have attempted to optimize a PET based adaptive threshold segmentation method for delineating small tumors, particularly in a background of high tracer activity. The metabolic nature of pituitary adenomas and the constraints of MRI imaging in the postoperative setting to delineate these tumors during radiosurgical procedures motivated us to develop this method. Phantom experiments were done to establish a relationship between the threshold required for segmenting the PET images and the target size and the activity concentration within the target in relation to its background. The threshold was developed from multiple linear regression of the experimental data optimized for tumor sizes less than 4 cm(3). We validated our method against the phantom target volumes with measured target to background ratios ranging from 1.6 to 14.58. The method was tested on ten retrospective patients with residual growth hormone-secreting pituitary adenomas that underwent radiosurgery and compared against the volumes delineated by manual method. The predicted volumes against the true volume of the phantom inserts gave a correlation coefficient of 99% (p < 0.01). In the ten retrospective patients, the automatically segmented tumor volumes against volumes manually delineated by the clinicians had a correlation of 94% (p < 0.01). This adaptive threshold segmentation showed promising results in delineating tumor volumes in pituitary adenomas planned for stereotactic radiosurgery, particularly in the postoperative setting where MR and CT images may be associated with artifacts, provided optimization experiment is carried out.

Infection is ubiquitous and a major cause of morbidity and mortality. The most reliable method for localizing infection requires radiolabeling autologous white blood cells ex vivo. A compound that can be injected directly into a patient and can selectively image infectious foci will eliminate the drawbacks. The resolution of infection is associated with neutrophil apoptosis and necrosis presenting phosphatidylserine (PS) on the neutrophil outer leaflet. Targeting PS with intravenous administration of a PS-specific, near-infrared (NIR) fluorophore will permit localization of infectious foci by optical imaging.

BACKGROUND AND OBJECTIVE:Cetuximab is a monoclonal antibody that binds to and inhibits the epidermal growth factor receptor (EGFR). EGFR overexpression has been observed in a subset of breast cancers. The purpose of this study was to evaluate 64Cu-labeled cetuximab as an imaging agent using MDA-MB-468 breast cancer cells.METHODS:Cetuximab was coupled with an N-sulfosuccinimide ester of DOTA, purified, and labeled with the positron-emitting nuclide, 64Cu. Receptor-binding specificity and affinity of 64Cu-DOTA-cetuximab were studied using human MDA-MB-468 breast cancer cells, which express high levels of EGFR. Micropositron emission tomography and biodistribution studies were performed in athymic nude mice bearing MDA-MB-468 cell xenografts. Blocking studies with cold cetuximab were also performed to determine the specific binding of cetuximab.RESULTS:The radiochemical yield was 97.1 ± 1.1%. The specific activity was 1.5 Ci/μm cetuximab and the affinity to EGFR-positive MDA-MB-468 cells was high (KD=0.4 nmol/l). Both biodistribution and micropositron emission tomographic imaging studies with 64Cu-DOTA-cetuximab showed higher tumor uptake at 24 h (20.91 ± 2.49% ID/g, standardized uptake values of 9.6) than at 4 h (11.65 ± 3.89% ID/g, standardized uptake values of 4.9). Tumor uptake was significantly reduced from 20.91 ± 2.49% ID/g at 24 h to 14.42 ± 0.85% ID/g in a 1-h blocking study (P=0.00).CONCLUSION:Cetuximab can be labeled with 64Cu without compromising its biological activity. The tumor uptake was excellent with high tumor/muscle (7.97 ± 1.78 at 4 h, 15.91 ± 6.04 at 24 h) and reasonable tumor/blood (0.5 ± 0.18 at 4 h, 2.12 ± 0.86 at 24 h) ratios. Blocking studies showed the specific binding of the labeled antibody to tumor tissue.

We are developing agents for positron emission tomography (PET) imaging of cancer gene mRNA expression and software to fuse mRNA PET images with anatomical computerized tomography (CT) images to enable volumetric (3D) haptic (touch-and-feel) simulation of pancreatic cancer and surrounding organs prior to surgery in a particular patient. We have identified a novel ligand specific for epidermal growth factor receptor (EGFR) to direct PET agent uptake specifically into cancer cells, and created a volumetric haptic surgical simulation of human pancreatic cancer reconstructed from patient CT data. Young's modulus and the Poisson ratio for each tissue will be adjusted to fit the experience of participating surgeons.

Erratum to: Targeting Apoptosis for Optical Imaging of Infection Mathew L. Thakur, Kaijun Zhang, Bishnuhari Paudyal, Devadhas Devakumar, Maria Y. Covarrubias, Chang-po Chen, Brian D. Gray, Eric Wickstrom, Koon Y. Pak Department of Radiology, Thomas Jefferson University, 1020 Locust Street, Suite 359-JAH, Philadelphia, PA 19107, USA Department of Biochemistry Molecular Biology, Thomas Jefferson University, Philadelphia, PA, USA Bioimaging, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA Molecular Targeting Technologies, Inc., West Chester, PA, USA

Despite the great strides made in imaging breast cancer (BC) in humans, the current imaging modalities miss up to 30% of BC, do not distinguish malignant lesions from benign ones, and require histologic examinations for which invasive biopsy must be performed. Annually in the United States, approximately 5.6 million biopsies find benign lesions. More than 50% of human BCs overexpress cyclin D1, and all BCs exhibit VPAC1 oncogene products. Together, these gene products may provide an excellent biomarker for the early and accurate detection of BC. We have evaluated 4 biologically active peptide analogs that have high affinity for VPAC1. The transgenic MMTVneu mice spontaneously develop BC and metastatic lesions that overexpress cyclin D1 and VPAC1 biomarkers. The MMTVneu mouse, therefore, provides an excellent animal model that mimics the pathogenesis of human BC. The objective of this investigation was to determine the ability of 1 of the peptide analogs, 64Cu-TP3805, to detect BC in MMTVneu mice using 18F-FDG as a gold standard. Methods: The transgenic MMTVneu mouse colony was maintained. Offspring were screened for transgenic status by reverse transcriptase polymerase chain reaction (RT-PCR). Nine mice with visible, palpable, or unknown metastatic lesions were entered into the protocol. 18F-FDG (6,475 ± 1,628 kBq [175 ± 44 μCi]) PET served as a control, followed by a CT scan and 24–48 h later by PET with 64Cu-TP3805 (4,588 ± 962 kBq [124 ± 26 μCi]). RT-PCR on excised tumors determined VPAC1 expression, and histology ascertained the pathology. Results: Ten tumors were detected by PET. Four tumors were detected both by 18F-FDG and by 64Cu-TP3805. Additionally, 4 tumors were imaged with 64Cu-TP3805 only. These 8 tumors overexpressed VPAC1 receptors and were malignant by histology. The 2 remaining tumors were visualized with 18F-FDG only. These tumors did not express the VPAC1 oncogene product and had benign histology. The standard uptake value ranged from 3.1 to 18.3 for 64Cu-TP3805 and 0.9 to 1.4 for 18F-FDG. Conclusion: 64Cu-TP3805 identified all malignant lesions unequivocally that overexpressed the VPAC1 oncogene surface product. The 2 benign tumors that did not express the VPAC1 receptor were not imaged. 64Cu-TP3805 promises to have the potential for the early and accurate imaging of primary and metastatic BC.

The prospective use for MV CT for patient treatment planning are for consecutive phase plans in 3D conformal and intensity modulated radiotherapy, patients with metal implants, adaptive radiation therapy and single fraction palliative treatment. This study is to validate the authors' previous work on MV cone beam CT (MV CBCT) images reconstructed with bespoke FDK algorithm based 3D reconstruction software for treatment planning. MV CBCT images were fused with planning CT images to include complimentary data which help elucidate visualization and segmentation of different anatomical structures for better treatment planning. The landmark based manual registration of planning CT and MV CBCT images was done. The registration was validated using quantitative and qualitative measures. A relative dose calculation was done where the whole brain was delineated as the target volume. Forward planning with parallel oppose fields with micro multi-leaf collimator shaping and 6 MV beam was performed on both kV and MV CBCT images. Dose volume histograms and volume comparisons of the target were done. The volumes of the target were measured on kV and MV CBCT. The relative dose for the points in the target volume in MV CBCT was within 3% difference from dose calculated with planning CT. The results of this study show that MV CBCT images could be effectively used for planning in a commercial treatment planning system, if the necessary corrections mentioned are incorporated.

Iodine-131 metaiodobenzyl guanidine ((131)I-MIBG) is routinely used for imaging and treatment of neuroendocrine tumors (NET). As the commercially available radiopharmaceutical was very expensive, we developed an in-house method of labeling MIBG with (131)I in 1993. A total of 247 batches of (131)I-MIBG were prepared and used in our hospital between April 1993 and September 2006. We report our experience over these 14 years of preparation of this tracer in our hospital radiopharmacy, for the scintigraphy of NET. The technique of preparation is simple and the labeled product was found to be of acceptable quality. With the routine availability and cost effectiveness, the utilization of this radiopharmaceutical for scintigraphy increased remarkably in our institution.