Application of the Lugano Classification for Initial Evaluation, Staging, and Response Assessment of Hodgkin and Non-Hodgkin Lymphoma: The PRoLoG Consensus Initiative (Part 1-Clinical).

Our objective was to provide consensus recommendations from a consortium of academic and industry experts in the field of lymphoma and imaging for consistent application of the Lugano classification. Methods: Consensus was obtained through a series of meetings from July 2019 until September 2021 sponsored by the Pharma Imaging Network for Therapeutics and Diagnostics (PINTaD) as part of the PINTaD Response Criteria in Lymphoma Working Group (PRoLoG) consensus initiative. Results: Consensus recommendations clarified technical considerations for PET/CT and diagnostic CT from the Lugano classification, including updating the FDG avidity of different lymphoma entities, clarifying the response nomenclature, and refining lesion classification and scoring, especially with regard to scores 4 and 5 and the X category of the 5-point scale. Combination of metabolic and anatomic responses is clarified, as well as response assessment in cases of discordant or missing evaluations. Use of clinical data in the classification, especially the requirement for bone marrow assessment, is further updated on the basis of lymphoma entities. Clarification is provided with regard to spleen and liver measurements and evaluation, as well as nodal response. Conclusion: Consensus recommendations are made to comprehensively address areas of inconsistency and ambiguity in the classification encountered during response evaluation by end users, and such guidance should be used as a companion to the 2014 Lugano classification.

Application of the Lugano Classification for Initial Evaluation, Staging, and Response Assessment of Hodgkin and Non-Hodgkin Lymphoma: The PRoLoG Consensus Initiative (Part 2-Technical).

The aim of this initiative was to provide consensus recommendations from a consortium of academic and industry experts in the field of lymphoma and imaging for the consistent application of imaging assessment with the Lugano classification. Methods: Consensus was obtained through a series of meetings from July 2019 to October 2021 sponsored by the PINTaD (Pharma Imaging Network for Therapeutics and Diagnostics) as part of the ProLoG (PINTaD RespOnse criteria in Lymphoma wOrking Group) consensus initiative. Results: Consensus recommendations encompass all technical imaging aspects of the Lugano classification. Some technical considerations for PET/CT and diagnostic CT are clarified with regards to required imaging series and scan visits, as well as acquisition and reconstruction of PET images and influence of lesion size and background activity. Recommendations are given on the role of imaging and clinical reviewers as well as on training and monitoring. Finally, an example template of an imaging case report form is provided to support efficient collection of data with Lugano Classification. Conclusion: Consensus recommendations are made to comprehensively address technical and imaging areas of inconsistency and ambiguity in the classification encountered by end users. Such guidance should be used to support standardized acquisition and evaluation with the Lugano 2014.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

RATIONALE AND OBJECTIVES: To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge. MATERIALS AND METHODS: The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2). The volumes of the real and simulated lesions were compared based on bias (measured mean volume differences between physical and virtually inserted lesions in phantoms as quantified by segmentation algorithms), repeatability, reproducibility, equivalence (phantom phase), and overall QIBA compliance (phantom and clinical phase). RESULTS: For phantom phase, three of eight groups were fully QIBA compliant, and one was marginally compliant. For compliant groups, the estimated biases were -1.8 ± 1.4%, -2.5 ± 1.1%, -3 ± 1%, -1.8 ± 1.5% (±95% confidence interval). No virtual insertion method showed statistical equivalence to physical insertion in bias equivalence testing using Schuirmann's two one-sided test (±5% equivalence margin). Differences in repeatability and reproducibility across physical and simulated lesions were largely comparable (0.1%-16% and 7%-18% differences, respectively). For clinical phase, 7 of 16 groups were QIBA compliant. CONCLUSION: Hybrid datasets yielded conclusions similar to real computed tomography datasets where phantom QIBA compliant was also compliant for hybrid datasets. Some groups deemed compliant for simulated methods, not for physical lesion measurements. The magnitude of this difference was small (<5.4%). While technical performance is not equivalent, they correlate, such that, volumetrically simulated lesions could potentially serve as practical proxies.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

RATIONALE AND OBJECTIVES: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS: Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

Importance of volumetric measurement processes in oncology imaging trials for screening and evaluation of tumors as per response evaluation criteria in solid tumors.

Cancer, like any disease, is a pathologic biological process. Drugs are designed to interfere with the pathologic process and should therefore also be validated using a functional screening method directed at these processes. Screening for cancers at an appropriate time and also evaluating results is also very important. Volumetric measurement helps in better screening and evaluation of tumors. Volumetry is a process of quantification of the tumors by identification (pre-cancerous or target lesion) and measurement. Volumetric image analysis allows an accurate, precise, sensitive, and medically valuable assessment of tumor response. It also helps in identifying possible outcomes such disease progression (PD) or complete response as per Response Evaluation Criteria in Solid Tumors (RECIST).