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2. Joint regional uptake quantification of Thorium-227 and Radium-223 using a multiple-energy-window projection-domain quantitative SPECT method

Author

Li, Zekun, Benabdallah, Nadia, Laforest, Richard, Wahl, Richard L., Thorek, Daniel L. J., and Jha, Abhinav K.

Subjects
Physics - Medical Physics
Abstract

Thorium-227-based alpha-particle radiopharmaceutical therapies ({\alpha}-RPTs) are being investigated in several clinical and pre-clinical studies. After administration, Thorium-227 decays to Radium-223, another alpha-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both Thorium-227 and Radium-223 is clinically important, and SPECT may perform this quantification as these isotopes also emit X- and gamma-ray photons. However, reliable quantification is challenged by the orders-of-magnitude lower activity compared to conventional SPECT, resulting in a very low number of detected counts, the presence of multiple photopeaks, substantial overlap in the emission spectra of these isotopes, and the image-degrading effects in SPECT. To address these issues, we propose a multiple-energy-window projection-domain quantification (MEW-PDQ) method that jointly estimates the regional activity uptake of both Thorium-227 and Radium-223 directly using the SPECT projection from multiple energy windows. We evaluated the method with realistic simulation studies using anthropomorphic digital phantoms, in the context of imaging patients with bone metastases of prostate cancer and treated with Thorium-227-based {\alpha}-RPTs. The proposed method yielded reliable (accurate and precise) regional uptake estimates of both isotopes and outperformed state-of-the-art methods across different lesion sizes and contrasts, in a virtual imaging trial, as well as with moderate levels of intra-regional heterogeneous uptake and with moderate inaccuracies in the definitions of the support of various regions. Additionally, we demonstrated the effectiveness of using multiple energy windows and the variance of the estimated uptake using the proposed method approached the Cram\'er-Rao-lower-bound-defined theoretical limit.

Published
2023

3. Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT

Author

Yu, Zitong, Rahman, Md Ashequr, Laforest, Richard, Schindler, Thomas H., Gropler, Robert J., Wahl, Richard L., Siegel, Barry A., and Jha, Abhinav K.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been using deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as RMSE and SSIM. However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; (3) demonstrate the utility of virtual clinical trials (VCTs) to evaluate DL-based methods. A VCT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. The impact of DL-based denoising was evaluated using fidelity-based FoMs and AUC, which quantified performance on detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. The results motivate the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VCTs provide a mechanism to conduct such evaluations using VCTs. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach.

Published
2023

5. Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)

Author

Rahmim, Arman, Bradshaw, Tyler J., Buvat, Irène, Dutta, Joyita, Jha, Abhinav K., Kinahan, Paul E., Li, Quanzheng, Liu, Chi, McCradden, Melissa D., Saboury, Babak, Siegel, Eliot, Sunderland, John J., and Wahl, Richard L.

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence
Abstract

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

Published
2022

6. Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

Author

Peehl, Donna M, Badea, Cristian T, Chenevert, Thomas L, Daldrup-Link, Heike E, Ding, Li, Dobrolecki, Lacey E, Houghton, A McGarry, Kinahan, Paul E, Kurhanewicz, John, Lewis, Michael T, Li, Shunqiang, Luker, Gary D, X., Cynthia, Manning, H Charles, Mowery, Yvonne M, O’Dwyer, Peter J, Pautler, Robia G, Rosen, Mark A, Roudi, Raheleh, Ross, Brian D, Shoghi, Kooresh I, Sriram, Renuka, Talpaz, Moshe, Wahl, Richard L, and Zhou, Rong

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Clinical Trials and Supportive Activities, Cancer, Clinical Research, Biomedical Imaging, Evaluation of treatments and therapeutic interventions, Detection, screening and diagnosis, 4.5 Resources and infrastructure (detection), 6.9 Resources and infrastructure (treatment evaluation), Good Health and Well Being, Animals, Mice, Humans, Neoplasms, Disease Models, Animal, Diagnostic Imaging, co-clinical trials, animal models, imaging, prostate cancer, sarcoma, colorectal cancer, osteosarcoma, pancreatic cancer, myelofibrosis, breast cancer, lung cancer
Abstract

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Published
2023

7. A deep learning algorithm for reducing false positives in screening mammography

Author

Pedemonte, Stefano, Tsue, Trevor, Mombourquette, Brent, Vu, Yen Nhi Truong, Matthews, Thomas, Hoil, Rodrigo Morales, Shah, Meet, Ghare, Nikita, Zingman-Daniels, Naomi, Holley, Susan, Appleton, Catherine M., Su, Jason, and Wahl, Richard L.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, and financial burden. This work demonstrates an AI algorithm that reduces false positives by identifying mammograms not suspicious for breast cancer. We trained the algorithm to determine the absence of cancer using 123,248 2D digital mammograms (6,161 cancers) and performed a retrospective study on 14,831 screening exams (1,026 cancers) from 15 US and 3 UK sites. Retrospective evaluation of the algorithm on the largest of the US sites (11,592 mammograms, 101 cancers) a) left the cancer detection rate unaffected (p=0.02, non-inferiority margin 0.25 cancers per 1000 exams), b) reduced callbacks for diagnostic exams by 31.1% compared to standard clinical readings, c) reduced benign needle biopsies by 7.4%, and d) reduced screening exams requiring radiologist interpretation by 41.6% in the simulated clinical workflow. This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.

Published
2022

8. Radiotherapy and theranostics: a Lancet Oncology Commission

Author

Abdel-Wahab, May, Giammarile, Francesco, Carrara, Mauro, Paez, Diana, Hricak, Hedvig, Ayati, Nayyereh, Li, Jing Jing, Mueller, Malina, Aggarwal, Ajay, Al-Ibraheem, Akram, Alkhatib, Sondos, Atun, Rifat, Bello, Abubakar, Berger, Daniel, Delgado Bolton, Roberto C, Buatti, John M, Burt, Graeme, Bjelac, Olivera Ciraj, Cordero-Mendez, Lisbeth, Dosanjh, Manjit, Eichler, Thomas, Fidarova, Elena, Gondhowiardjo, Soehartati, Gospodarowicz, Mary, Grover, Surbhi, Hande, Varsha, Harsdorf-Enderndorf, Ekaterina, Herrmann, Ken, Hofman, Michael S, Holmberg, Ola, Jaffray, David, Knoll, Peter, Kunikowska, Jolanta, Lewis, Jason S, Lievens, Yolande, Mikhail-Lette, Miriam, Ostwald, Dennis, Palta, Jatinder R, Peristeris, Platon, Rosa, Arthur A, Salem, Soha Ahmed, dos Santos, Marcos A, Sathekge, Mike M, Shrivastava, Shyam Kishore, Titovich, Egor, Urbain, Jean-Luc, Vanderpuye, Verna, Wahl, Richard L, Yu, Jennifer S, Zaghloul, Mohamed Saad, Zhu, Hongcheng, and Scott, Andrew M

Published
2024
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10. A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy

Author

Li, Zekun, Benabdallah, Nadia, Abou, Diane S., Baumann, Brian C., Dehdashti, Farrokh, Ballard, David H., Liu, Jonathan, Jammalamadaka, Uday, Laforest, Richard, Wahl, Richard L., Thorek, Daniel L. J., and Jha, Abhinav K.

Subjects
Physics - Medical Physics
Abstract

Single-photon emission computed tomography (SPECT) provides a mechanism to estimate regional isotope uptake in lesions and at-risk organs after administration of {\alpha}-particle-emitting radiopharmaceutical therapies ({\alpha}-RPTs). However, this estimation task is challenging due to the complex emission spectra, the very low number of detected counts, the impact of stray-radiation-related noise at these low counts, and the multiple image-degrading processes in SPECT. The conventional reconstruction-based quantification methods are observed to be erroneous for {\alpha}-RPT SPECT. To address these challenges, we developed a low-count quantitative SPECT (LC-QSPECT) method that directly estimates the regional activity uptake from the projection data, compensates for stray-radiation-related noise, and for the radioisotope and SPECT physics. The method was validated in the context of three-dimensional SPECT with 223 Ra. Validation was performed using both realistic simulation studies, including a virtual clinical trial, and synthetic and anthropomorphic physical-phantom studies. Across all studies, the LC-QSPECT method yielded reliable regional-uptake estimates and outperformed the conventional ordered subset expectation maximization (OSEM)-based reconstruction and geometric transfer matrix (GTM)-based post-reconstruction partial-volume compensation methods. Further, the method yielded reliable uptake across different lesion sizes, contrasts, and different levels of intra-lesion heterogeneity. Additionally, the variance of the estimated uptake approached the Cram\'e-Rao bound-defined theoretical limit.

Published
2021

11. A Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images

Author

Matthews, Thomas P., Singh, Sadanand, Mombourquette, Brent, Su, Jason, Shah, Meet P., Pedemonte, Stefano, Long, Aaron, Maffit, David, Gurney, Jenny, Hoil, Rodrigo Morales, Ghare, Nikita, Smith, Douglas, Moore, Stephen M., Marks, Susan C., and Wahl, Richard L.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T45, I.5.4, J.3, I.2.10, I.4.8
Abstract

Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multi-site setting for synthetic two-dimensional mammography (SM) images derived from digital breast tomosynthesis exams using full-field digital mammography (FFDM) images and limited SM data. Materials and Methods: A DL model was trained to predict BI-RADS breast density using FFDM images acquired from 2008 to 2017 (Site 1: 57492 patients, 187627 exams, 750752 images) for this retrospective study. The FFDM model was evaluated using SM datasets from two institutions (Site 1: 3842 patients, 3866 exams, 14472 images, acquired from 2016 to 2017; Site 2: 7557 patients, 16283 exams, 63973 images, 2015 to 2019). Each of the three datasets were then split into training, validation, and test datasets. Adaptation methods were investigated to improve performance on the SM datasets and the effect of dataset size on each adaptation method is considered. Statistical significance was assessed using confidence intervals (CI), estimated by bootstrapping. Results: Without adaptation, the model demonstrated substantial agreement with the original reporting radiologists for all three datasets (Site 1 FFDM: linearly-weighted $\kappa_w$ = 0.75 [95% CI: 0.74, 0.76]; Site 1 SM: $\kappa_w$ = 0.71 [95% CI: 0.64, 0.78]; Site 2 SM: $\kappa_w$ = 0.72 [95% CI: 0.70, 0.75]). With adaptation, performance improved for Site 2 (Site 1: $\kappa_w$ = 0.72 [95% CI: 0.66, 0.79], 0.71 vs 0.72, P = .80; Site 2: $\kappa_w$ = 0.79 [95% CI: 0.76, 0.81], 0.72 vs 0.79, P $<$ .001) using only 500 SM images from that site. Conclusion: A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved using few SM images.

Published
2020

12. Overview of the First NRG Oncology–National Cancer Institute Workshop on Dosimetry of Systemic Radiopharmaceutical Therapy

Author

Roncali, Emilie, Capala, Jacek, Benedict, Stanley H, Akabani, Gamal, Bednarz, Bryan, Bhadrasain, Vikram, Bolch, Wesley E, Buchsbaum, Jeffrey C, Coleman, Norman C, Dewaraja, Yuni K, Frey, Eric, Ghaly, Michael, Grudzinski, Joseph, Hobbs, Robert F, Howell, Roger W, Humm, John L, Kunos, Charles A, Larson, Steve, Lin, Frank I, Madsen, Mark, Mirzadeh, Saed, Morse, David, Pryma, Daniel, Sgouros, George, St James, Sara, Wahl, Richard L, Xiao, Ying, Zanzonico, Pat, and Zukotynski, Katherine

Subjects
Cancer, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, National Cancer Institute (U.S.), Neoplasms, Radiometry, United States, radiopharmaceutical therapy, targeted radionuclide therapy, dosimetry, microdosimetry, cellular dosimetry, MIRD, Clinical Sciences, Nuclear Medicine & Medical Imaging
Abstract

In 2018, the National Cancer Institute and NRG Oncology partnered for the first time to host a joint workshop on systemic radiopharmaceutical therapy (RPT) to specifically address dosimetry issues and strategies for future clinical trials. The workshop focused on current dosimetric approaches for clinical trials, strategies under development that would optimize dose reporting, and future desired or optimized approaches for novel emerging radionuclides and carriers in development. In this article, we review the main approaches that are applied clinically to calculate the absorbed dose. These include absorbed doses calculated over a variety of spatial scales, including whole body, organ, suborgan, and voxel, the last 3 of which are achievable within the MIRD schema (S value) and can be calculated with analytic methods or Monte Carlo methods, the latter in most circumstances. This article will also contrast currently available methods and tools with those used in the past, to propose a pathway whereby dosimetry helps the field by optimizing the biologic effect of the treatment and trial design in the drug approval process to reduce financial and logistical costs. We also briefly discuss the dosimetric equivalent of biomarkers to help bring a precision medicine approach to RPT implementation when merited by evidence collected during early-phase trial investigations. Advances in the methodology and related tools have made dosimetry the optimum biomarker for RPT.

Published
2021

13. Radionuclide Therapy of Lymphomas

Author

Parihar, Ashwin Singh, Jacene, Heather A., Tirumani, Sree Harsha, Wahl, Richard L., Volterrani, Duccio, editor, Erba, Paola A., editor, Strauss, H. William, editor, Mariani, Giuliano, editor, and Larson, Steven M., editor

Published
2022
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15. RNA-Seq based gene expression profiling of baseline and on-treatment breast tumors to predict response to HER2-directed therapy, without chemotherapy (TBCRC026).

Author

Hennessy, Maeve, primary, Fernández, Aranzazu, additional, Huang, Chiung Yu, additional, Cimino-Mathews, Ashley, additional, Denbow, Rita, additional, Abramson, Vandana G, additional, Rimawi, Mothaffar F., additional, Specht, Jennifer M., additional, Storniolo, Anna Maria, additional, Valero, Vicente, additional, Vaklavas, Chris, additional, Winer, Eric P., additional, Krop, Ian E., additional, Wolff, Antonio C., additional, Wahl, Richard L., additional, Thompson, E Aubrey, additional, Stearns, Vered, additional, Perou, Charles, additional, Carey, Lisa A., additional, and Connolly, Roisin M., additional

Published
2024
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17. Spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography and magnetic resonance diffusion imaging metrics in cervical cancer

Author

Floberg, John M, Fowler, Kathryn J, Fuser, Dominique, DeWees, Todd A, Dehdashti, Farrokh, Siegel, Barry A, Wahl, Richard L, Schwarz, Julie K, and Grigsby, Perry W

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Cancer, Clinical Research, Rare Diseases, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, PET/MRI, Multimodal imaging, Diffusion imaging, Cervical cancer, Imaging biomarkers, Medical Biochemistry and Metabolomics, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundThis study investigated the spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography ([18F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data. We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [18F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUVmax), mean ADC (ADCmean), and mean tumor-to-muscle ADC ratio (ADCT/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis.ResultsSeventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUVmax and ADCmean, and SUVmax and ADCT/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS).ConclusionsIn this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS.

Published
2018

19. The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Author

Press, Robert H, Shu, Hui-Kuo G, Shim, Hyunsuk, Mountz, James M, Kurland, Brenda F, Wahl, Richard L, Jones, Ella F, Hylton, Nola M, Gerstner, Elizabeth R, Nordstrom, Robert J, Henderson, Lori, Kurdziel, Karen A, Vikram, Bhadrasain, Jacobs, Michael A, Holdhoff, Matthias, Taylor, Edward, Jaffray, David A, Schwartz, Lawrence H, Mankoff, David A, Kinahan, Paul E, Linden, Hannah M, Lambin, Philippe, Dilling, Thomas J, Rubin, Daniel L, Hadjiiski, Lubomir, and Buatti, John M

Subjects
Medical and Biological Physics, Physical Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Research, Clinical Trials and Supportive Activities, Good Health and Well Being, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Neoplasms, Positron-Emission Tomography, Radiation Oncology, Tomography, X-Ray Computed, Tumor Hypoxia, Other Physical Sciences, Clinical Sciences, Oncology & Carcinogenesis, Oncology and carcinogenesis, Theoretical and computational chemistry, Medical and biological physics
Abstract

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Published
2018

20. A Semiautonomous Deep Learning System to Reduce False-Positive Findings in Screening Mammography

Author

Pedemonte, Stefano, primary, Tsue, Trevor, additional, Mombourquette, Brent, additional, Truong Vu, Yen Nhi, additional, Matthews, Thomas, additional, Morales Hoil, Rodrigo, additional, Shah, Meet, additional, Ghare, Nikita, additional, Zingman-Daniels, Naomi, additional, Holley, Susan, additional, Appleton, Catherine M., additional, Su, Jason, additional, and Wahl, Richard L., additional

Published
2024
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21. Diagnosis of Stage IV Melanoma

Author

Tarhini, Ahmad A., Agarwala, Sanjiv S., Khunger, Arjun, Wahl, Richard L., Balch, Charles M., Balch, Charles M., editor, Atkins, Michael B., editor, Garbe, Claus, editor, Gershenwald, Jeffrey E., editor, Halpern, Allan C., editor, Kirkwood, John M., editor, McArthur, Grant A., editor, Thompson, John F., editor, and Sober, Arthur J., editor

Published
2020
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25. Initial Experience with Tositumomab and I-131-Labeled Tositumomab for Treatment of Relapsed/Refractory Hodgkin Lymphoma

Author

Jacene, Heather, Crandall, John, Kasamon, Yvette L, Ambinder, Richard F, Piantadosi, Steven, Serena, Donna, Kasecamp, Wayne, and Wahl, Richard L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Hematology, Cancer, Clinical Research, Rare Diseases, Lymphoma, Orphan Drug, Biomedical Imaging, Adult, Antibodies, Monoclonal, Cohort Studies, Dose-Response Relationship, Radiation, Female, Hodgkin Disease, Humans, Male, Middle Aged, Neoplasm Recurrence, Local, Treatment Outcome, Whole-Body Irradiation, Young Adult, [I-131]tositumomab, Hodgkin lymphoma, Hodgkin, FDG, Bexxar, [131I]tositumomab, Physiology, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

PurposeTo determine the maximum tolerated dose (MTD) of [131I]tositumomab in patients with refractory/recurrent Hodgkin lymphoma (HL) and to preliminarily determine if [131I]tositumomab has activity against HL and if positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]DG) performed 6 weeks post-therapy predicted 12-week response.ProceduresSeparate dose-finding studies were performed for patients with and without prior transplant. A single therapeutic total body radiation dose (TBD) of [131I]tositumomab was administered. TBD was escalated/de-escalated based on dose-limiting hematologic toxicity (DLT) using a modified continual reassessment method. [18F]DG-PET/CT scans were performed at baseline and 6 and 12 weeks post therapy.ResultsTwelve patients (nine classical HL, three lymphocyte-predominant [LP] HL) completed two dosing levels (n = 3 each) in the post-transplant (55 cGy, 79 cGy) and no transplant (75 cGy, 87 cGy) groups. Hematologic toxicities were common and transient. Twelve weeks after [131I]tositumomab, 10 patients progressed and two with LPHL achieved complete response. [18F]DG-PET/CT at 6 weeks post therapy appeared more predictive than CT at 6 weeks of a response at 12 weeks.ConclusionsTositumomab and [131I]tositumomab was well-tolerated in patients with relapsed/refractory HL. Complete responses in LPHL support a therapeutic effect in this subtype. Early metabolic response assessments by [18F]DG-PET in HL after radioimmunotherapy appear to be more predictive than purely anatomic assessments.

Published
2017

28. Imaging Melanoma

Author

Wahl, Richard L., Flaherty, Keith T., Section editor, Bastian, Boris C., Section editor, Tsao, Hensin, Section editor, Hodi, F. Stephen, Section editor, Fisher, David E., editor, and Bastian, Boris C., editor

Published
2019
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30. Dose-Response Demonstrated for Durable Complete Remission Following High-Dose Targeted Radiation with 131I-Apamistamab Prior to HCT in Patients with R/R AML

Author

Chen, George L, primary, Abboud, Camille, additional, Gyurkocza, Boglarka, additional, Nath, Rajneesh, additional, Seropian, Stuart, additional, Choe, Hannah, additional, Litzow, Mark R., additional, Koshy, Nebu, additional, Stiff, Patrick J., additional, Tomlinson, Benjamin, additional, Abhyankar, Sunil H., additional, Foran, James, additional, Abedin, Sameem, additional, Al-Kadhimi, Zaid, additional, Kebriaei, Partow, additional, Sabloff, Mitchell, additional, Orozco, Johnnie J., additional, Jamieson, Katarzyna, additional, Magalhaes-Silverman, Margarida, additional, van Besien, Koen, additional, Schuster, Michael W, additional, Law, Arjun D., additional, Leung, Eugene, additional, Chen, Ming-Kai, additional, Natwa, Mona P, additional, Spross, Jennifer, additional, Li, Kate L, additional, Desai, Avinash, additional, Wahl, Richard L, additional, Brodin, Patrik, additional, and Pandit-Taskar, Neeta, additional

Published
2024
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33. Quantitative Imaging in Cancer Clinical Trials

Author

Yankeelov, Thomas E, Mankoff, David A, Schwartz, Lawrence H, Lieberman, Frank S, Buatti, John M, Mountz, James M, Erickson, Bradley J, Fennessy, Fiona MM, Huang, Wei, Kalpathy-Cramer, Jayashree, Wahl, Richard L, Linden, Hannah M, Kinahan, Paul E, Zhao, Binsheng, Hylton, Nola M, Gillies, Robert J, Clarke, Laurence, Nordstrom, Robert, and Rubin, Daniel L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Trials and Supportive Activities, Clinical Research, Biomedical Imaging, Bioengineering, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, 4.4 Population screening, Good Health and Well Being, Clinical Trials as Topic, Evaluation Studies as Topic, Humans, Molecular Imaging, Molecular Targeted Therapy, Neoplasms, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies.

Published
2016

34. Contributors

Author

Aarntzen, Erik, primary, Achilefu, Samuel, additional, Akam, Eman A., additional, Albaghdadi, Mazen, additional, Beer, Ambros J., additional, Bharti, Santosh, additional, Bhujwalla, Zaver M., additional, Bischof, Gérard N., additional, Biswal, Sandip, additional, Boss, Marti, additional, Botnar, René M., additional, Brinson, Zabecca, additional, Brom, Maarten, additional, Buitinga, Mijke, additional, Bulte, Jeff W.M., additional, Caravan, Peter, additional, Chan, Heang-Ping, additional, Chandy, Mark, additional, Chaney, Aisling M., additional, Chen, Delphine L., additional, Chen, Xiaoyuan (Shawn), additional, Chenevert, Thomas L., additional, Coughlin, Jennifer M., additional, Covington, Matthew F., additional, Cumming, Paul, additional, Daldrup-Link, Heike E., additional, Deal, Emily M., additional, de Galan, Bastiaan, additional, Derlin, Thorsten, additional, Dewhirst, Mark W., additional, Di Paolo, Arianna, additional, Drzezga, Alexander, additional, Du, Yong, additional, Thi-Quynh Duong, Mai, additional, Ehman, Richard L., additional, Eriksson, Olof, additional, Galli, Filippo, additional, Gatenby, Robert A., additional, Gelovani, Juri, additional, Giehl, Kathrin, additional, Giger, Maryellen L., additional, Goel, Reema, additional, Gold, Garry, additional, Gotthardt, Martin, additional, Graham, Michael M., additional, Gropler, Robert J., additional, Gründer, Gerhard, additional, Gulhane, Avanti, additional, Hadjiiski, Lubomir, additional, Hajhosseiny, Reza, additional, Hammoud, Dima A., additional, Helfer, Brooke M., additional, Hicks, Rodney J., additional, Higuchi, Takahiro, additional, Hoffman, John M., additional, Honer, Michael, additional, Huang, Sung-Cheng (Henry), additional, Hung, Jessica, additional, Hwang, Do Won, additional, Jackson, Isaac M., additional, Jacobs, Andreas H., additional, Jaffer, Farouc A., additional, Jain, Sanjay K., additional, James, Michelle L., additional, Jansen, Tom, additional, Johansson, Lars, additional, Joosten, Lieke, additional, Kakkad, Samata, additional, Kamson, David, additional, Kang, Sae-Ryung, additional, Kelly, Kimberly A., additional, Knopp, Michelle I., additional, Knopp, Michael V., additional, Kogan, Feliks, additional, Krishnamachary, Balaji, additional, Künnecke, Basil, additional, Lee, Dong Soo, additional, Libby, Peter, additional, Luker, Gary D., additional, Luker, Kathryn E., additional, Makowski, Marcus R., additional, Mankoff, David A., additional, Massoud, Tarik F., additional, Meyer, Charles R., additional, Miller, Zach, additional, Min, Jung-Joon, additional, Mondal, Suman B., additional, Montesi, Sydney B., additional, Navin, Patrick J., additional, Nekolla, Stephan G., additional, Niu, Gang, additional, Notohamiprodjo, Susan, additional, Ordoñez, Alvaro A., additional, Osborn, Eric A., additional, Pacheco-Torres, Jesus, additional, Pagano, Gennaro, additional, Palmer, Gregory M., additional, Paulmurugan, Ramasamy, additional, Penet, Marie-France, additional, Phinikaridou, Alkystis, additional, Pomper, Martin G., additional, Prieto, Claudia, additional, Qi, Haikun, additional, Raghunand, Natarajan, additional, Ramar, Thangam, additional, Reynolds, Fred, additional, Ropella-Panagis, Kathleen, additional, Ross, Brian D., additional, Rowe, Steven P., additional, Rudin, Markus, additional, Sadaghiani, Mohammad S., additional, Sager, Hendrik, additional, Samala, Ravi, additional, Saraste, Antti, additional, Schelhaas, Sonja, additional, Schwaiger, Markus, additional, Schwarz, Sally W., additional, Seiberlich, Nicole, additional, Shapiro, Mikhail G., additional, Shim, Hyunsuk, additional, Signore, Alberto, additional, Solnes, Lilja B., additional, Suh, Minseok, additional, Tsien, Christina, additional, van Eimeren, Thilo, additional, Varasteh, Zohreh, additional, Venkatesh, Sudhakar Kundapur, additional, Viel, Thomas, additional, Waerzeggers, Yannic, additional, Wahl, Richard L., additional, Weber, Wolfgang, additional, Werner, Rudolf A., additional, Winkeler, Alexandra, additional, Wong, Dean F., additional, Wright, Chadwick L., additional, Wu, Anna M., additional, Wu, Joseph C., additional, Yoon, Daehyun, additional, You, Sung-Hwan, additional, Yuan, Chun, additional, Yuan, Hong, additional, Zanzonico, Pat, additional, Zhao, Xue-Qiao, additional, Zhou, Iris Y., additional, and Zinnhardt, Bastian, additional

Published
2021
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37. Head and Neck PET/CT: Therapy Response Interpretation Criteria (Hopkins Criteria)—Interreader Reliability, Accuracy, and Survival Outcomes

Author

Marcus, Charles, Ciarallo, Anthony, Tahari, Abdel K, Mena, Esther, Koch, Wayne, Wahl, Richard L, Kiess, Ana P, Kang, Hyunseok, and Subramaniam, Rathan M

Subjects
Cancer, Dental/Oral and Craniofacial Disease, Biomedical Imaging, Rare Diseases, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Adult, Aged, Carcinoma, Squamous Cell, Female, Fluorodeoxyglucose F18, Head and Neck Neoplasms, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Multimodal Imaging, Positron-Emission Tomography, Proportional Hazards Models, Retrospective Studies, Squamous Cell Carcinoma of Head and Neck, Tomography, X-Ray Computed, Treatment Outcome, Hopkins PET interpretation criteria, head and neck, therapy assessment, Clinical Sciences, Nuclear Medicine & Medical Imaging
Abstract

UnlabelledThere has been no established qualitative system of interpretation for therapy response assessment using PET/CT for head and neck cancers. The objective of this study was to validate the Hopkins interpretation system to assess therapy response and survival outcome in head and neck squamous cell cancer patients (HNSCC).MethodsThe study included 214 biopsy-proven HNSCC patients who underwent a posttherapy PET/CT study, between 5 and 24 wk after completion of treatment. The median follow-up was 27 mo. PET/CT studies were interpreted by 3 nuclear medicine physicians, independently. The studies were scored using a qualitative 5-point scale, for the primary tumor, for the right and left neck, and for overall assessment. Scores 1, 2, and 3 were considered negative for tumors, and scores 4 and 5 were considered positive for tumors. The Cohen κ coefficient (κ) was calculated to measure interreader agreement. Overall survival (OS) and progression-free survival (PFS) were analyzed by Kaplan-Meier plots with a Mantel-Cox log-rank test and Gehan Breslow Wilcoxon test for comparisons.ResultsOf the 214 patients, 175 were men and 39 were women. There was 85.98%, 95.33%, 93.46%, and 87.38% agreement between the readers for overall, left neck, right neck, and primary tumor site response scores, respectively. The corresponding κ coefficients for interreader agreement between readers were, 0.69-0.79, 0.68-0.83, 0.69-0.87, and 0.79-0.86 for overall, left neck, right neck, and primary tumor site response, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of the therapy assessment were 68.1%, 92.2%, 71.1%, 91.1%, and 86.9%, respectively. Cox multivariate regression analysis showed human papillomavirus (HPV) status and PET/CT interpretation were the only factors associated with PFS and OS. Among the HPV-positive patients (n = 123), there was a significant difference in PFS (hazard ratio [HR], 0.14; 95% confidence interval, 0.03-0.57; P = 0.0063) and OS (HR, 0.01; 95% confidence interval, 0.00-0.13; P = 0.0006) between the patients who had a score negative for residual tumor versus positive for residual tumor. A similar significant difference was observed in PFS and OS for all patients. There was also a significant difference in the PFS of patients with PET-avid residual disease in one site versus multiple sites in the neck (HR, 0.23; log-rank P = 0.004).ConclusionThe Hopkins 5-point qualitative therapy response interpretation criteria for head and neck PET/CT has substantial interreader agreement and excellent negative predictive value and predicts OS and PFS in patients with HPV-positive HNSCC.

Published
2014

38. Patlak Slope versus Standardized Uptake Value Image Quality in an Oncologic PET/CT Population: A Prospective Cross-Sectional Study.

Author

Ince, Semra, Laforest, Richard, Itani, Malak, Prasad, Vikas, Ashrafinia, Saeed, Smith, Anne M., Wahl, Richard L., and Fraum, Tyler J.

Subjects
POSITRON emission tomography, WILCOXON signed-rank test, CROSS-sectional method, LONGITUDINAL method, LIKERT scale
Abstract

Patlak slope (PS) images have the potential to improve lesion conspicuity compared with standardized uptake value (SUV) images but may be more artifact-prone. This study compared PS versus SUV image quality and hepatic tumor-to-background ratios (TBRs) at matched time points. Early and late SUV and PS images were reconstructed from dynamic positron emission tomography (PET) data. Two independent, blinded readers scored image quality metrics (a four-point Likert scale) and counted tracer-avid lesions. Hepatic lesions and parenchyma were segmented and quantitatively analyzed. Differences were assessed via the Wilcoxon signed-rank test (alpha, 0.05). Forty-three subjects were included. For overall quality and lesion detection, early PS images were significantly inferior to other reconstructions. For overall quality, late PS images (reader 1 [R1]: 3.95, reader 2 [R2]: 3.95) were similar (p > 0.05) to early SUV images (R1: 3.88, R2: 3.84) but slightly superior (p ≤ 0.002) to late SUV images (R1: 2.97, R2: 3.44). For lesion detection, late PS images were slightly inferior to late SUV images (R1 only) but slightly superior to early SUV images (both readers). PS-based TBRs were significantly higher than SUV-based TBRs at the early time point, with opposite findings at the late time point. In conclusion, late PS images are similar to early/late SUV images in image quality and lesion detection; the superiority of SUV versus PS hepatic TBRs is time-dependent. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

Author

Herington, Jonathan, primary, McCradden, Melissa D., additional, Creel, Kathleen, additional, Boellaard, Ronald, additional, Jones, Elizabeth C., additional, Jha, Abhinav K., additional, Rahmim, Arman, additional, Scott, Peter J.H., additional, Sunderland, John J., additional, Wahl, Richard L., additional, Zuehlsdorff, Sven, additional, and Saboury, Babak, additional

Published
2023
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41. Contributors

Author

Abbruzzese, James L., primary, Abdel-Wahab, Omar, additional, Abou-Alfa, Ghassan K., additional, Abrahm, Janet L., additional, Abrams, Jeffrey S., additional, Abramson, Jeremy S., additional, Aisner, Dara L., additional, Alonso-Basanta, Michelle, additional, Anampa, Jesus, additional, Anderson, Megan E., additional, Antonarakis, Emmanuel S., additional, Aplenc, Richard, additional, Appelbaum, Frederick R., additional, Araujo, Luiz H., additional, Asban, Ammar, additional, Ashwood, Edward, additional, Awan, Farrukh T., additional, Aylward, Juliet L., additional, Balar, Arjun V., additional, Balentine, Courtney J., additional, Barta, Stefan K., additional, Bartlett, Nancy, additional, Basen-Engquist, Karen, additional, Beaupin, Lynda Kwon, additional, Berkowitz, Ross S., additional, Berry, Donald A., additional, Bevers, Therese, additional, Boggess, John F., additional, Brahmer, Julie R., additional, Brown, Janet, additional, Brown, Karen, additional, Brown, Powel, additional, Browner, Ilene, additional, Bunn, Paul A., additional, Burns, William R., additional, Byrd, John C., additional, Cadoo, Karen, additional, Carbone, David P., additional, Carter, H. Ballentine, additional, Castillo, Jorge J., additional, Chang, Alfred E., additional, Chang, Eric, additional, Chanock, Stephen J., additional, Chapuy, Claudia I., additional, Chauhan, Vikash P., additional, Chen, Herbert, additional, Chen, Ronald C., additional, Cheung, Nai-Kong V., additional, Choe, Jennifer H., additional, Christian, Michaele C., additional, Cinciripini, Paul M., additional, Clarke, Michael F., additional, Coleman, Robert E., additional, Coleman, Robert L., additional, Coletta, Adriana M., additional, Collins, Jerry M., additional, Connors, Jean M., additional, Cools, Michael, additional, Coombes, Kevin R., additional, Cortes, Jorge, additional, Costa, Mauro W., additional, Covey, Anne, additional, Cowan, Kenneth H., additional, Crane, Christopher H., additional, Crawford, Jeffrey, additional, Crooks, Kristy, additional, Culkin, Daniel J., additional, Czito, Brian G., additional, Dalerba, Piero, additional, Dalmau, Josep, additional, Dang, Mai, additional, D'Angelica, Michael, additional, Davies, Kurtis D., additional, Davis, Myrtle, additional, Dea, Nicolas, additional, De Jesus-Acosta, Ana, additional, DeMarzo, Angelo M., additional, DeWeese, Theodore L., additional, Diehn, Maximilian, additional, Digumarthy, Subba R., additional, Dispenzieri, Angela, additional, Do, Khanh T., additional, Dobrenkov, Konstantin, additional, Dome, Jeffrey S., additional, Doroshow, James H., additional, Dorsey, Jay F., additional, Dubard-Gault, Marianne, additional, DuBois, Steven G., additional, Duda, Dan G., additional, Dunlop, Malcolm, additional, Duska, Linda R., additional, Duvic, Madeleine, additional, El Dika, Imane, additional, El-Serag, Hashem, additional, Engelmann, Jeffrey M., additional, Ettinger, David S., additional, Fashoyin-Aje, Lola A., additional, Fearon, Eric R., additional, Ford, James M., additional, Franklin, Wilbur A., additional, Freer, Phoebe E., additional, Freidlin, Boris, additional, Freifeld, Alison G., additional, Friedlander, Terence W., additional, Friedman, Debra L., additional, Fuller, Arian F., additional, Galluzzi, Lorenzo, additional, Gebhardt, Mark C., additional, George, Daniel J., additional, Geyer, Mark B., additional, Giaccia, Amato J., additional, Gilbert, Mark R., additional, Goldner, Whitney, additional, Goldstein, Donald P., additional, Goodman, Annekathryn, additional, Goodman, Karyn A., additional, Gordon, Kathleen, additional, Graeff-Armas, Laura, additional, Greenstein, Alexander J., additional, Grossman, Stuart A., additional, Grupp, Stephan, additional, Gupta, Arjun, additional, Haider, Irfanullah, additional, Haigentz, Missak, additional, Hainsworth, John D., additional, Haithcock, Benjamin E., additional, Hallemeier, Christopher L., additional, Hanash, Samir, additional, Hanrahan, Aphrothiti J., additional, Harding, James, additional, Harrison, Michael R., additional, Hasham, Muneer G., additional, Hawk, Ernest, additional, Hayman, Jonathan, additional, Heinlen, Jonathan E., additional, Henry, N. Lynn, additional, Herman, Joseph, additional, Hobbs, Brian P., additional, Holen, Ingunn, additional, Horn, Leora, additional, Horowitz, Neil S., additional, Horwitz, Steven M., additional, Houghton, Odette, additional, Howard, Scott C., additional, Hudis, Clifford A., additional, Hunger, Stephen P., additional, Hurria, Arti, additional, Ilson, David H., additional, Im, Annie, additional, Iyer, Gopa, additional, Jaffee, Elizabeth M., additional, Jagsi, Reshma, additional, Jain, Rakesh K., additional, Jarnagin, William, additional, Jatoi, Aminah, additional, Jhingran, Anuja, additional, Johnson, David H., additional, Johnston, Brian, additional, Johnston, Patrick G., additional, Judy, Kevin D., additional, Kachnic, Lisa A., additional, Kaidar-Person, Orit, additional, Kalva, Sanjeeva, additional, Kamin, Deborah Y., additional, Kantarjian, Hagop, additional, Karakousis, Giorgos, additional, Karam-Hage, Maher, additional, Kaskas, Nadine M., additional, Kastan, Michael B., additional, Katabi, Nora, additional, Kaul, Daniel R., additional, Kelley, Scott R., additional, Kemeny, Nancy, additional, Kent, Erin E., additional, Kepp, Oliver, additional, Khagi, Simon, additional, Kilgore, Joshua E., additional, Kim, D. Nathan, additional, Kleinschmidt-DeMasters, Bette K., additional, Korn, Edward L., additional, Kroemer, Guido, additional, Ku, Geoffrey Y., additional, Kummar, Shivaani, additional, Ky, Bonnie, additional, Laheru, Daniel A., additional, Lambert, Paul F., additional, Lawler, Mark, additional, Le-Rademacher, Jennifer G., additional, Lee, John Y.K., additional, Lee, Nancy Y., additional, Lee, Susanna L., additional, Leeman, Jonathan E., additional, Linkermann, Andreas, additional, Liu, Jinsong, additional, Lo, Simon, additional, Locasale, Jason W., additional, Loprinzi, Charles L., additional, Lowery, Maeve, additional, Ludwig, Emmy, additional, Lunning, Matthew A., additional, Lustig, Robert A., additional, Machtay, Mitchell, additional, Mackall, Crystal, additional, Mahvi, David A., additional, Mahvi, David M., additional, Maity, Amit, additional, Majithia, Neil, additional, Malumbres, Marcos, additional, Maresso, Karen Colbert, additional, Martin, John D., additional, Matsuo, Koji, additional, Matthews, Natalie H., additional, Mauro, Lauren, additional, Mayer, R. Samuel, additional, McCaskill-Stevens, Worta, additional, McNamara, Megan A., additional, Mehta-Shah, Neha, additional, Merritt, Robert E., additional, Milowsky, Matthew I., additional, Minasian, Lori M., additional, Mitchell, Tara C., additional, Mitsis, Demytra, additional, Mollica, Michelle, additional, Mooney, Margaret, additional, Moustafa, Farah, additional, Nabati, Lida, additional, Naidoo, Jarushka, additional, Narang, Amol, additional, Nelson, Heidi, additional, Nelson, William G., additional, Nesbit, Suzanne, additional, Niglas, Mark, additional, O'Connor, Tracey, additional, Offit, Kenneth, additional, Onciu, Mihaela, additional, O’Reilly, Eileen M., additional, Ostrander, Elaine A., additional, Pappas-Taffer, Lisa, additional, Pardoll, Drew, additional, Park, Jae H., additional, Patel, Anery, additional, Patel, Anish J., additional, Patierno, Steven R., additional, Pavletic, Steven Z., additional, Phillips, Peter C., additional, Post, Miriam D., additional, Pruitt, Amy A., additional, Querfeld, Christiane, additional, Rabius, Vance A., additional, Rajkumar, S. Vincent, additional, Ramadan, Mohammad O., additional, Rankin, Erinn B., additional, Reddy, Sushanth, additional, Reid, Michael A., additional, Reznik, Scott, additional, Rizack, Tina, additional, Robinson, Jason D., additional, Robinson-Bostom, Leslie, additional, Rodriguez-Galindo, Carlos, additional, Romesser, Paul B., additional, Rosen, Steven T., additional, Rosenfeld, Myrna R., additional, Rosenthal, Nadia, additional, Ross, Meredith, additional, Rowland, Julia H., additional, Russell, Anthony H., additional, Sabel, Michael S., additional, Sahgal, Arjun, additional, Salinas, Ryan D., additional, Salo-Mullen, Erin E., additional, Salto-Tellez, Manuel, additional, Sanderson, Sydney M., additional, Sandlund, John T., additional, Santana, Victor M., additional, Savage, Michelle, additional, Schreiber, Eric C., additional, Schuchter, Lynn, additional, Schultz, Liora, additional, Seiden, Michael V., additional, Sellers, Morgan M., additional, Shah, Payal D., additional, Shia, Jinru, additional, Shilo, Konstantin, additional, Small, Eric, additional, Smith, Angela B., additional, Snow, Stephen N., additional, Solit, David B., additional, Sood, Anil K., additional, Soto-Perez-de-Celis, Enrique, additional, Sparano, Joseph A., additional, Spiegelman, Vladimir S., additional, Spunt, Sheri L., additional, Stadler, Zsofia K., additional, Steensma, David P., additional, Stone, Richard M., additional, Stranne, Steven Kent, additional, Stratton, Kelly, additional, Sugden, Bill, additional, Swanson, Andrew M., additional, Tallman, Martin S., additional, Talmadge, James E., additional, Teachey, David T., additional, Teba, Catalina V., additional, Tefferi, Ayalew, additional, Teh, Bin Tean, additional, Teng, Joyce M.C., additional, Tepper, Joel E., additional, Thaker, Premal H., additional, Thrift, Aaron P., additional, Tran, Arthur-Quan, additional, Triska, Grace, additional, Trump, Donald, additional, Tsai, Kenneth, additional, Tseng, Chia-Lin, additional, Tseng, Diane, additional, Van Schaeybroeck, Sandra, additional, Van Tine, Brian A., additional, Vanness, Erin R., additional, Varadhachary, Gauri, additional, Varella-Garcia, Marileila, additional, Wahl, Richard L., additional, Walsh, Michael F., additional, Wang, Thomas, additional, Weiss, Jared, additional, Weissman, Irving L., additional, Westin, Shannon N., additional, White, Jeffrey D., additional, Wilson, Richard, additional, Wong, Richard J., additional, Wood, Gary S., additional, Xu, Yaohui G., additional, Xu-Welliver, Meng, additional, Yust-Katz, Shlomit, additional, Zagar, Timothy, additional, Zeman, Elaine M., additional, Zhang, Tian, additional, and Zwiebel, James A., additional

Published
2020
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43. Radionuclide Therapy of Lymphomas

Author

Jacene, Heather A., Tirumani, Sree Harsha, Wahl, Richard L., Strauss, H. William, editor, Mariani, Giuliano, editor, Volterrani, Duccio, editor, and Larson, Steven M., editor

Published
2017
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48. Correlation of SUV on Early Interim PET with Recurrence-Free Survival and Overall Survival in Primary Operable HER2-Positive Breast Cancer (the TBCRC026 Trial)

Author

Hennessy, Maeve A., primary, Leal, Jeffrey P., additional, Huang, Chiung-Yu, additional, Solnes, Lilja B., additional, Denbow, Rita, additional, Abramson, Vandana G., additional, Carey, Lisa A., additional, Liu, Minetta C., additional, Rimawi, Mothaffar, additional, Specht, Jennifer, additional, Storniolo, Anna Maria, additional, Valero, Vicente, additional, Vaklavas, Christos, additional, Winer, Eric P., additional, Krop, Ian E., additional, Wolff, Antonio C., additional, Cimino-Mathews, Ashley, additional, Wahl, Richard L., additional, Stearns, Vered, additional, and Connolly, Roisin M., additional

Published
2023
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49. Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance

Author

Herington, Jonathan, primary, McCradden, Melissa D., additional, Creel, Kathleen, additional, Boellaard, Ronald, additional, Jones, Elizabeth C., additional, Jha, Abhinav K., additional, Rahmim, Arman, additional, Scott, Peter J.H., additional, Sunderland, John J., additional, Wahl, Richard L., additional, Zuehlsdorff, Sven, additional, and Saboury, Babak, additional

Published
2023
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