Primary Tumor Delineation Research Articles

Evaluating Virtual Contrast-Enhanced Magnetic Resonance Imaging in Nasopharyngeal Carcinoma Radiation Therapy: A Retrospective Analysis for Primary Gross Tumor Delineation

PurposeTo investigate the potential of virtual contrast-enhanced MRI (VCE-MRI) for gross-tumor-volume (GTV) delineation of nasopharyngeal carcinoma (NPC) using multi-institutional data. Methods and MaterialsThis study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI) and planning CT of 348 biopsy-proven NPC patients from three oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and two medical physicists participated in clinical evaluations in three aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively. ResultsThe mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes were less than 1Gy. ConclusionsThe VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients.

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.

Impact of consensus guidelines on delineation of primary tumor clinical target volume (CTVp) for head and neck cancer: Results of a national review project

Background and purposeA significant interobserver variability (IOV) for clinical target volume of the primary tumor (CTVp) delineation was shown in a previous national review project. Since then, international expert consensus guidelines (CG) for the delineation of CTVp were published. The aim of this follow-up study was to 1) objectify the extent of implementation of the CG, 2) assess its impact on delineation quality and consistency, 3) identify any remaining ambiguities. Materials and methodsAll Belgian RT departments were invited to complete an online survey and submit CTVp for 5 reference cases. Organs at risk and GTV of the primary tumor were predefined. Margins, volumes, IOV between all participating centers (IOVall) and IOV compared to a reference consensus delineation (IOVref) were calculated and compared to the previous analysis. A qualitative analysis was performed assessing the correct interpretation of the CG for each case. Results17 RT centers completed both survey and delineations, of which 88% had implemented CG. Median DSCref for CTVp_total was 0.80–0.92. IOVall and IOVref improved significantly for the centers following CG (p = 0.005). IOVref for CTVp_high was small with a DSC higher than 0.90 for all cases. A significant volume decrease for the CTVp receiving 70 Gy was observed. Interpretation of CG was more accurate for (supra)glottic carcinoma. 60% of the radiation oncologists thinks clarification of CG is indicated. ConclusionImplementation of consensus guidelines for CTVp delineation is already fairly advanced on a national level, resulting in significantly increased delineation uniformity. The accompanying substantial decrease of CTV receiving high dose RT calls for caution and correct interpretation of CG. Clarification of the existing guidelines seems appropriate especially for oropharyngeal and hypopharyngeal carcinoma.

The Role of MRI and PET/CT in Radiotherapy Target Volume Determination in Gastrointestinal Cancers-Review of the Literature.

Positron emission tomography with computed tomography (PET/CT) and magnetic resonance imaging (MRI) could improve accuracy in target volume determination for gastrointestinal cancers. A systematic search of the PubMed database was performed, focusing on studies published within the last 20 years. Articles were considered eligible for the review if they included patients with anal canal, esophageal, rectal or pancreatic cancer, as well as PET/CT or MRI for radiotherapy treatment planning, and if they reported interobserver variability or changes in treatment planning volume due to different imaging modalities or correlation between the imaging modality and histopathologic specimen. The search of the literature retrieved 1396 articles. We retrieved six articles from an additional search of the reference lists of related articles. Forty-one studies were included in the final review. PET/CT seems indispensable for target volume determination of pathological lymph nodes in esophageal and anal canal cancer. MRI seems appropriate for the delineation of primary tumors in the pelvis as rectal and anal canal cancer. Delineation of the target volumes for radiotherapy of pancreatic cancer remains challenging, and additional studies are needed.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [^{18}F]FDG PET/CT

PurposePET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients.MethodsAutomated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [^{18}F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [^{18}F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively.ResultsIn the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) (text {HR}_{text {man}}=1.9; p<0.001 vs. text {HR}_{text {cnn}}=1.8; p<0.001 in cross-validation and text {HR}_{text {man}}=1.8; p=0.011 vs. text {HR}_{text {cnn}}=1.9; p=0.004 in external testing).ConclusionTo the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Ferumoxytol: Another Exciting Addition to the Growing Field of Bone Marrow Imaging.

Ferumoxytol: Another Exciting Addition to the Growing Field of Bone Marrow Imaging.

Nanoparticles Targeted to Fibroblast Activation Protein Outperform PSMA for MRI Delineation of Primary Prostate Tumors.

Accurate delineation of gross tumor volumes remains a barrier to radiotherapy dose escalation and boost dosing in the treatment of solid tumors, such as prostate cancer.Magneticresonance imaging (MRI)of tumor targets has the power to enable focal dose boosting, particularly when combined with technological advances such as MRI-linearaccelerator. Fibroblast activation protein (FAP) is overexpressed in stromal components of >90% of epithelial carcinomas. Herein, the authors compare targeted MRI of prostatespecificmembraneantigen (PSMA) with FAP in the delineation of orthotopic prostate tumors. Control, FAP, and PSMA-targeting iron oxide nanoparticleswereprepared with modification of a lymphotropic MRI agent (FerroTrace, Ferronova). Mice with orthotopic LNCaP tumors underwent MRI 24 h after intravenous injection of nanoparticles. FAP and PSMA nanoparticles produced contrast enhancement on MRI when compared to control nanoparticles. FAP-targeted MRI increased the proportion of tumor contrast-enhancing black pixels by 13%, compared to PSMA. Analysis of changes in R2 values between healthy prostates and LNCaP tumors indicated an increase in contrast-enhancing pixels in the tumor border of 15% when targeting FAP, compared to PSMA. This study demonstrates the preclinical feasibility of PSMA and FAP-targeted MRI which can enable targeted image-guided focal therapy of localized prostate cancer.

Evaluation of optimal monoenergetic images acquired by dual-energy CT in the diagnosis of T staging of thoracic esophageal cancer

ObjectivesThe purpose of our study was to objectively and subjectively assess optimal monoenergetic image (MEI (+)) characteristics from dual-energy CT (DECT) and the diagnostic performance for the T staging in patients with thoracic esophageal cancer (EC).MethodsIn this retrospective study, patients with histopathologically confirmed EC who underwent DECT from September 2019 to December 2020 were enrolled. One standard polyenergetic image (PEI) and five MEI (+) were reconstructed. Two readers independently assessed the lesion conspicuity subjectively and calculated the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of EC. Two readers independently assessed the T stage on the optimal MEI (+) and PEI subjectively. Multiple quantitative parameters were measured to assess the diagnostic performance to identify T1-2 from T3-4 in EC patients.ResultsThe study included 68 patients. Subjectively, primary tumor delineation received the highest ratings in MEI (+) 40 keV of the venous phase. Objectively, MEI (+) images showed significantly higher SNR compared with PEI (p < 0.05), peaking at MEI (+) 40 keV in the venous phase. CNR of tumor (MEI (+) 40 keV -80 keV) was all significantly higher than PEI in arterial and venous phases (p < 0.05), peaking at MEI (+) 40 keV in venous phases. The agreement between MEI (+) 40 keV and pathologic T categories was 81.63% (40/49). Rho values in venous phases had excellent diagnostic efficiency for identifying T1-2 from T3-4 (AUC = 0.84).ConclusionsMEI (+) reconstructions at low keV in the venous phase improved the assessment of lesion conspicuity and also have great potential for preoperative assessment of T staging in patients with EC.

Reduction of Inter-Observer Variability Using MRI and CT Fusion in Delineating of Primary Tumor for Radiotherapy in Lung Cancer with Atelectasis

<h3>Purpose/Objective(s)</h3> To compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis. Accurately identify the outline volume of the tumor and reduce the probability of normal tissue complications. <h3>Materials/Methods</h3> Retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All patients were scanned with MRI and CT in the same supine position within one week. All positioning images were transferred to treatment planning systems. According to the anatomy of sternum, rib, vertebral body, pulmonary artery and tumor, each MRI sequence (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) was automatically registered with CT through the planning system, and the fusion stability was increased by manual correction. Five radiation oncologists with rich clinical experience delineate tumor boundary to obtain the gross tumor volume (GTV). The boundary between tumor and atelectasis was determined by referring to CT structure, coronal and sagittal MRI images when drawing CT-MRI fusion images. The target areas of CT and MRI images were sketched on the same day. Consistence index (CI) and dice coefficient (DC) were used to measure differences among observers. <h3>Results</h3> The differences of delineation mean volumes, CI and DC among CT and MRI were significant. Multiple comparisons were made between MRI sequences and CT. The mean volume of DWI, T2WI, T1WI + C 10 minutes sequence in the target area is significantly smaller than that on CT sequence, but the consistency is higher than that of CT sequences. <h3>Conclusion</h3> The recognition of atelectasis by MRI was better than that by CT, which could reduce inter-observer variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis. Abbreviations: CT: Computed tomography; GTV: Gross tumor volume; MRI: Magnetic resonance imaging; T1WI: WATER:BH AX LAVA-Flex; T1WI+C: WATER:BH AX LAVA-Flex-C; T1WI+C Delay: WATER:BH AX LAVA-Flex-C delay; T1WI+C 10 minutes: WATER:BH AX LAVA-Flex-C 10min; T2WI:RTr Ax T2 fs Propeller; CI: Consistence index; DC: Dice coefficient.

Impact of endorectal filling on interobserver variability of MRI based rectal primary tumor delineation

BackgroundOnline adaptive MR-guided radiotherapy allows for the reduction of safety margins in dose escalated treatment of rectal tumors. With the use of smaller margins, precise tumor delineation becomes more critical. In the present study we investigated the impact of rectal ultrasound gel filling on interobserver variability in delineation of primary rectal tumor volumes. MethodsSix patients with locally advanced rectal cancer were scanned on a 1.5 T MRI-Linac without (MRI_e) and with application of 100 cc of ultrasound gel transanally (MRI_f). Eight international radiation oncologists expert in the treatment of gastrointestinal cancers delineated the gross tumor volume (GTV) on both MRI scans. MRI_f scans were provided to the participating centers after MRI_e scans had been returned. Interobserver variability was analyzed by either comparing the observers’ delineations with a reference delineation (approach 1) and by building all possible pairs between observers (approach 2). Dice Similarity Index (DICE) and 95 % Hausdorff-Distance (95 %HD) were calculated. ResultsRectal ultrasound gel filling was well tolerated by all patients. Overall, interobserver agreement was superior in MRI_f scans based on median DICE (0.81 vs 0.74, p < 0.005 for approach 1 and 0.76 vs 0.64, p < 0.0001 for approach 2) and 95 %HD (6.9 mm vs 4.2 mm for approach 1, p = 0.04 and 8.9 mm vs 6.1 mm, p = 0.04 for approach 2). Delineated median tumor volumes and inter-quartile ranges were 26.99 cc [18.01–50.34 cc] in MRI_e and 44.20 [19.72–61.59 cc] in MRI_f scans respectively, p = 0.012. ConclusionsAlthough limited by the small number of patients, in this study the application of rectal ultrasound gel resulted in higher interobserver agreement in rectal GTV delineation. The endorectal gel filling might be a useful tool for future dose escalation strategies.

Establishment of a Prediction Model for Overall Survival after Stereotactic Body Radiation Therapy for Primary Non-Small Cell Lung Cancer Using Radiomics Analysis

Simple SummaryLung cancer remains the leading cause of cancer-related mortality worldwide. Although early-stage non-small cell lung cancer (NSCLC) is likely to be controlled with stereotactic body radiation therapy (SBRT), approximately 18% of patients lead to recurrence. The aim of this study was to identify prognostic factors and establish a predictive model for survival outcomes of patients with non-metastatic NSCLC treated with SBRT. Several radiomic features were selected as predictive factors and two prediction models were established from the pre-treatment computed tomography images of 250 patients in the training cohort. One radiomic factor remained a significant prognostic factor of overall survival (OS) (p = 0.044), and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used.Stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) leads to recurrence in approximately 18% of patients. We aimed to extract the radiomic features, with which we predicted clinical outcomes and to establish predictive models. Patients with primary non-metastatic NSCLC who were treated with SBRT between 2002 and 2022 were retrospectively reviewed. The 358 primary tumors were randomly divided into a training cohort of 250 tumors and a validation cohort of 108 tumors. Clinical features and 744 radiomic features derived from primary tumor delineation on pre-treatment computed tomography were examined as prognostic factors of survival outcomes by univariate and multivariate analyses in the training cohort. Predictive models of survival outcomes were established from the results of the multivariate analysis in the training cohort. The selected radiomic features and prediction models were tested in a validation cohort. We found that one radiomic feature showed a significant difference in overall survival (OS) in the validation cohort (p = 0.044) and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used.

Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis

PurposeTo compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis.MethodA retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All positioning images were transferred to Eclipse treatment planning systems (TPSs). Six MRI sequences (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) were registered with positioning CT. Five radiation oncologists delineated the tumor boundary to obtain the gross tumor volume (GTV). Conformity index (CI) and dice coefficient (DC) were used to measure differences among observers.ResultsThe differences in delineation mean volumes, CI, and DC among CT and MRIs were significant. Multiple comparisons were made between MRI sequences and CT. Among them, DWI, T2WI, and T1WI+C 10 minutes sequences were statistically significant with CT in mean volumes, DC, and CI. The mean volume of DWI, T2WI, and T1WI+C 10 minutes sequence in the target area is significantly smaller than that on the CT sequence, but the consistency is higher than that of CT sequences.ConclusionsThe recognition of atelectasis by MRI was better than that by CT, which could reduce interobserver variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis.

Strengths, weaknesses, opportunities, and threat analysis of dual-energy CT in head and neck imaging

Technological development of dual-energy computed tomography (DECT) can play an important role in head and neck area. Multiple innovative applications have evolved, optimizing images, achieving metallic artifact reduction, differentiating materials with better primary tumor delineation, thyroid cartilage and bone invasion. Furthermore, quantification algorithms allow measuring iodine concentration, reflecting the blood supply of a lesion indirectly.DECT enables acquiring images with lower radiation doses and iodine intravenous contrast load to obtain the same CT values.. However, DECT uses ionizing radiation, which does not occur with MRI, and requires long post-processing times. Artifacts on iodine maps may be a potential source of pseudolesions. Besides, photon-counting CT scanners are a promising technique that may displace some DECT advantages.A review analyzing the current status of DECT applied to head and neck imaging from the scope of strengths, weaknesses, opportunities, and threatsanalysis would be very interesting to facilitate a realistic, fact-based, data-driven look of this technique.

Análisis de las fortalezas, oportunidades, debilidades y amenazas de la tomografía computarizada de doble energía en el diagnóstico por la imagen de la cabeza y el cuello

Technological development of dual-energy computed tomography (DECT) can play an important role in head and neck area. Multiple innovative applications have evolved, optimizing images, achieving metallic artifact reduction, differentiating materials with better primary tumor delineation, thyroid cartilage and bone invasion. Furthermore, quantification algorithms allow measuring iodine concentration, reflecting the blood supply of a lesion indirectly.DECT enables acquiring images with lower radiation doses and iodine intravenous contrast load to obtain the same CT values. However, DECT uses ionizing radiation, which does not occur with MRI, and requires long post-processing times. Artifacts on iodine maps may be a potential source of pseudolesions. Besides, photon-counting CT scanners are a promising technique that may displace some DECT advantages.A review analyzing the current status of DECT applied to head and neck imaging from the scope of strengths, weaknesses, opportunities, and threatsanalysis would be very interesting to facilitate a realistic, fact-based, data-driven look of this technique.

Comparison of 68Ga-FAPI and 18F-FDG PET/CT in the Evaluation of Advanced Lung Cancer.

Background Gallium 68 (68Ga)-labeled fibroblast-activation protein inhibitor (FAPI) has recently been introduced as a promising tumor imaging agent. Purpose To compare 68Ga-FAPI PET/CT with fluorine 18 (18F)-labeled fluorodeoxyglucose (FDG) PET/CT in evaluating lung cancer. Materials and Methods In this prospective study conducted from September 2020 to February 2021, images from participants with lung cancer who underwent both 68Ga-FAPI and 18F-FDG PET/CT examinations were analyzed. The tracer uptakes, quantified by maximum standardized uptake value (SUVmax) and target-to-background ratio (TBR), were compared for paired positive lesions between both modalities using the paired t test or Wilcoxon signed-rank test. Results Thirty-four participants (median age, 64 years [interquartile range: 46-80 years]; 20 men) were evaluated. From visual evaluation, 68Ga-FAPI PET/CT and 18F-FDG PET/CT showed similar performance in the delineation of primary tumors and detection of suspected metastases in the lungs, liver, and adrenal glands. The metabolic tumor volume in primary and recurrent lung tumors showed no difference between modalities (mean: 11.6 vs 10.8, respectively; P = .68). However, compared with 18F-FDG PET/CT, 68Ga-FAPI PET/CT depicted more suspected metastases in lymph nodes (356 vs 320), brain (23 vs 10), bone (109 vs 91), and pleura (66 vs 35). From semiquantitative evaluation, the SUVmax and TBR of primary or recurrent tumors, positive lymph nodes, bone lesions, and pleural lesions at 68Ga-FAPI PET/CT were all higher than those at 18F-FDG PET/CT (all P < .01). Although SUVmax of 68Ga-FAPI and 18F-FDG in brain metastases were not different (mean SUVmax: 9.0 vs 7.4, P = .32), TBR was higher with 68Ga-FAPI than with 18F-FDG (mean: 314.4 vs 1.0, P = .02). Conclusion Gallium 68-labeled fibroblast-activation protein inhibitor PET/CT may outperform fluorine 18-labeled fluorodeoxyglucose PET/CT in staging lung cancer, particularly in the detection of metastasis to the brain, lymph nodes, bone, and pleura. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Jacobson and Van den Abbeele in this issue.

A Crowdsourcing Challenge for the Development of Artificial Intelligence Algorithms for Automated Identification and Delineation of Pancreatic Cancer Primary Tumors

To develop a crowdsourced artificial intelligence computational algorithm to accurately identify and delineate pancreatic cancer (PC) primary tumors and major peripancreatic vessels on high-resolution CT images.After IRB approval, a well-curated data set of 243 diagnostic pancreatic-protocol high-resolution CT scans of patients diagnosed with PC was prepared. A data science marathon competition was then hosted online by a third-party company between December 2020 and January 2021. The full data set was randomly partitioned into training, validation and hold-out (scoring) data sets. Contestants were provided a training dataset containing [de-identified files from a high-resolution IV contrast-enhanced CT DICOM image set; de-identified DICOM metadata; de-identified RT structure set file with contours of the tumor and superior mesenteric artery (SMA), celiac axis/common hepatic artery continuum (CA/CHA), and superior mesenteric vein/portal vein continuum (SMV/PV)], along with a smaller validation data set with CT scans without contours to test their trained algorithms. Provisional scores were assigned using the validation data set throughout the competition. Final scoring of submitted algorithms was based on the performance of the algorithms on a hold-out data set (unavailable for the contestants), matched against the expected ground truth data using an F1 scoring metric [2 * precision * recall / (precision + recall)], in which precision = TP / (TP + FP), and recall = TP / (TP + FN). TP is the area (measured in pixels) of the overlap of expected (ground truth delineation) and extracted (submitted algorithm) regions, FP is the area extracted but which does not belong to expected regions, FN is the area of the expected regions that is not covered by extracted regions. The above was calculated separately for the 4 structure types (tumor, SMA, CA/CHA, and SMV/PV), with a weighted average of the four F1 values, where the weight of the tumor was 7, and the weight of each of the vessels was 1. A final average for each contestant was scaled up to 0-100 range.In total, 337 algorithm solutions from 23 competitors were submitted. The winning algorithm achieved an average F1 score of 70.2. The F1 score ranged from 69.8 to 66.7 between the 2nd and 5th best solutions. The winning solution used a 3D-UNet model to develop a 3D semantic segmentation approach that was found to be significantly accurate on tumor and vessel delineation. A comparison between the winning solution and an ensemble between the 2nd to 5th place solutions showed that in 82% of cases, both the winning and the ensembled solution succeeded in achieving an F1 score > 60.A crowdsourced innovation challenge was successfully employed to generate artificial intelligence algorithms capable of accurately delineating pancreatic cancer on a diagnostic CT scan.

Fluorodeoxyglucose positron-emission tomography-magnetic resonance hybrid imaging: An emerging tool for staging of cancer of the uterine cervix.

Positron-emission tomography-magnetic resonance imaging (PET-MRI) is an emerging hybrid imaging modality that utilizes the superior soft tissue resolution of MR with the metabolic data from PET. In this study, we sought to assess the clinical value of fluorodeoxyglucose (FDG) PET-MRI with dedicated pelvic PET-MR in the initial staging of cervical cancer. In this institutional-approved study, we identified 23 adult females who underwent FDG PET-MRI on hybrid camera for staging of primary uterine cervical cancer that included a dedicated PET-MR of the pelvis. A nuclear medicine physician and a radiologist reviewed the PET, MRI, and fusion-body and pelvis images alone and then with consensus read characterizing PET and MR abnormal findings. There were 23 patients who underwent FDG PET-MRI for initial staging of cervical cancer with an average age of 52.2 ± 14.0 years. A total of 23 suspected lymph nodes in eight different patients were detected within the pelvis with increased metabolic activity on PET. Both the dedicated pelvis and whole-body PET imaging detected the same corresponding pelvic lymph nodes, although the pelvic PET imaging had better lymph node uptake delineation due to longer acquisition time. Using a 10-mm short-axis criterion, MRI identified only 43.5% of the FDG avid lymph nodes. The average SUVmax on the pelvis PET sequences was higher with SUV 8.9 ± 5.2 compared to the whole-body PET with SUV 7.8 ± 5.4 but was not statistically significant (P > 0.05). Primary cervical cancer was identified in 18 patients on both PET imaging and MRI with dedicated MR pelvis providing better characterization. Based on our results of the patients with cervical cancer evaluated for initial staging, combining dedicated pelvic PET-MRI with whole-body PET/MR provides the most complete status of malignant disease in reference to delineation of primary tumor, involvement of surrounding tissues, and regional lymph nodes.

Value of spectral detector CT for pretherapeutic, locoregional assessment of esophageal cancer

PurposeTo investigate the diagnostic value of spectral detector dual-energy CT-derived low-keV virtual monoenergetic images (VMI) and iodine overlays (IO) for locoregional, pretherapeutic assessment of esophageal cancer. Method74 patients with biopsy-proven esophageal cancer who underwent pre-therapeutic, portal-venous-phase staging examinations of the chest and abdomen were retrospectively included. Quantitative image analysis was performed ROI-based within the tumor, healthy esophageal wall, peri-esophageal lymph nodes, azygos vein, aorta, liver, diaphragm, and mediastinal fat. Two radiologists evaluated delineation of the primary tumor and locoregional lymph nodes, assessment of the celiac trunk and diagnostic certainty regarding tumor infiltration in conventional images (CI), VMI from 40 to 70 keV and IO. Moreover, presence/absence of advanced tumor infiltration (T3/T4) was determined binary using all available images. ResultsVMI40−60keV showed significantly higher attenuation and signal-to-noise ratio compared to CI for all assessed ROIs, peaking at VMI40keV (p < 0.05). Contrast-to-noise ratio of tumor/esophagus (VMI40keV/CI: 7.7 ± 4.7 vs. 2.3 ± 1.5), tumor/diaphragm (VMI40keV/CI: 9.0 ± 5.5 vs. 2.2 ± 1.7) and tumor/liver (4.3 ± 5.5 vs. 1.9 ± 2.1) were all significantly higher compared to CI (p < 0.05). Qualitatively, lymph node delineation and diagnostic certainty regarding tumor infiltration received highest ratings both in IO and VMI40keV, whereas vascular assessment was rated highest in VMI40keV and primary tumor delineation in IO. Sensitivity/Specificity/Accuracy for detecting advanced tumor infiltration using the combination of CI, VMI40−70keV and IO was 42.4 %/82.0 %/56.3 %. ConclusionsIO and VMI40−60keV improve qualitative assessment of the primary tumor and depiction of lymph nodes and vessels at pretherapeutic SDCT of esophageal cancer patients yet do not mitigate the limitations of CT in determining tumor infiltration.

Label-free multiphoton imaging allows brain tumor recognition based on texture analysis-a study of 382 tumor patients.

BackgroundLabel-free multiphoton microscopy has been suggested for intraoperative recognition and delineation of brain tumors. For any future clinical application, appropriate approaches for image acquisition and analysis have to be developed. Moreover, an evaluation of the reliability of the approach, taking into account inter- and intrapatient variability, is needed.MethodsCoherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation were acquired on cryosections of brain tumors of 382 patients and 28 human nontumor brain samples. Texture parameters of those images were calculated and used as input for linear discriminant analysis.ResultsThe combined analysis of texture parameters of the CARS and TPEF signal proved to be most suited for the discrimination of nontumor brain versus brain tumors (low- and high-grade astrocytoma, oligodendroglioma, glioblastoma, recurrent glioblastoma, brain metastases of lung, colon, renal, and breast cancer and of malignant melanoma) leading to a correct rate of 96% (sensitivity: 96%, specificity: 100%). To approximate the clinical setting, the results were validated on 42 fresh, unfixed tumor biopsies. 82% of the tumors and, most important, all of the nontumor samples were correctly recognized. An image resolution of 1 µm was sufficient to distinguish brain tumors and nontumor brain. Moreover, the vast majority of single fields of view of each patient’s sample were correctly classified with high probabilities, which is important for clinical translation.ConclusionLabel-free multiphoton imaging might allow fast and accurate intraoperative delineation of primary and secondary brain tumors in combination with endoscopic systems.

PET/MRI-guided GTV delineation during radiotherapy planning in patients with squamous cell carcinoma of the tongue

PurposeThe aim of the study was to evaluate the usefulness and accuracy of 18-fluorine-labeled fluorodeoxyglucose (PET) and magnetic resonance imaging (MRI) hybrid in gross tumor volume (GTV) delineation during radiotherapy planning in patients with carcinoma of the tongue.MethodsTen patients with squamous cell carcinoma (SCC) of the tongue underwent computed tomography (CT) and PET/MRI examination. The GTV for primary tumor and lymph nodes (nGTV) were defined on CT (GTV-CT) and compared to GTVs obtained from PET (GTV-PET) and MRI (GTV-MRI) images. Two methods of GTV determination were used: visual interpretation of CT, PET (GTV-PETvis) and MRI images and quantitative automatic method (Syngovia, Siemens) based on a chosen threshold value (20%, 30%, 40%, 50%) of standardized uptake values (SUVmax) from PET examination (GTV-PET20%, GTV-PET30%, etc.). Statistical analysis of differences in GTV values obtained from CT, PET and MRI studies was performed. GTV-CT was used as a reference.ResultsIn all, 80% of GTV-MRI and 40% of GTV-PETvis were larger than GTV-CT. Respectively, 20% of GTV-MRI and 60% of GTV-PETvis were smaller than GTV-CT. Taking into account all threshold measurements, 70% of volumes were smaller than GTV-CT. GTV-PET30% were the most closely related volumes to GTV-CT from all threshold methods in 50% of patients. GTV-PETvis generated the most similar volumes in relation to GTV-CT from all PET measurements. Statistical analysis confirmed those results. Compared to nGTV-CT, 70% of nGTV-MRI and 20% of nGTV-PETvis were larger. The remaining nGTV-MRI and nGTV-PETvis measurements were smaller than nGTV-CT. Measurements of all thresholds nGTVs were smaller than nGTV-CTV in 52.5% of cases. nGTV-PET20% were the most closely related volumes to nGTV-CT in 40% of the cases. Statistical analysis showed that nGTV-PET20% (p = 0.0468), nGTV-PETvis (p = 0.0166), and nGTV-PET50% (p = 0.0166) diverge significantly from nGTV-CT results. nGTV-MRI (p = 0.1141), nGTV-PET30% (p = 0.2845), and nGTV-PET40% (p = 0.5076) were significantly related with nGTV-CT.ConclusionCombination of PET/MRI provides more information during target tumor mass delineation in radiotherapy planning of patients with SCC of the tongue than other standard imaging methods. The most frequently matching threshold value was 30% of SUVmax for primary tumor delineation and 30–40% of SUVmax for nGTV determination.