Prognostic value of baseline metabolic tumor volume (<scp>MTV</scp>) for forecasting chemotherapy outcome in early‐stage unfavorable Hodgkin lymphoma: Data from the phase <scp>III HD17</scp> trial

van Heek, Lutz; Weindler, Jasmin; Gorniak, Claudia; Kaul, Helen; Müller, Horst; Mettler, Jasmin; Baues, Christian; Fuchs, Michael; Borchmann, Peter; Ferdinandus, Justin; Dietlein, Markus; Voltin, Conrad‐Amadeus; Kobe, Carsten; Roth, Katrin S.

doi:10.1111/ejh.14093

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Metabolic tumour volume (MTV), which is the metabolically active volume of the tumour determined on baseline PET-scans, has proven to be a moderate predictive factor in both early favourable and early unfavourable cHL when treated with conventional combined modalities [24,25 ▪ ,26]. The calculation method has however not yet been standardized and is therefore not reproductible in clinical practice.…”

Section: Improving Prognostic Markersmentioning

confidence: 99%

How to treat localized Hodgkin lymphoma?

Bernard,

André,

Crochet

2024

Current Opinion in Oncology

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Purpose of review We aim to summarize the current knowledge on the management of early-stage classical Hodgkin lymphoma, with a focus on conventional strategies, incorporation of immunotherapies and exploration of novel prognostic markers. Recent findings Long-term data on combined modalities (associating chemotherapy and radiotherapy) still supports their benefit in terms of progression free survival compared to chemotherapy alone in both early favourable and early unfavourable interim PET-negative classical Hodgkin Lymphoma. Novel agents, such as Brentuximab Vedotin and checkpoints inhibitors show promising and impressive results when added to first-line treatment. Various strategies have been used, mainly in phase 2 non randomized clinical trials. Interim PET-scan has limited prognostic value and its role in regimens incorporating immunotherapies is yet unknown. Other prognosis markers emerge, such as metabolic tumour volume and circulating tumour DNA. By reflecting tumour burden pretreatment and minimal residual disease on treatment, they might be useful tools guiding treatment decisions. Summary Novel immunotherapy agents are likely to change the landscape in front-line management of classical early-stage Hodgkin lymphoma by combined modality treatment. Despite encouraging recent data, proof of their efficacy and safety on the longer term are still needed. Treatment decisions might be guided by new promising prognosis markers but their use in clinical practice is still to be determined.

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Section: Improving Prognostic Markersmentioning

confidence: 99%

How to treat localized Hodgkin lymphoma?

Bernard,

André,

Crochet

2024

Current Opinion in Oncology

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PET/CT Reconstruction and Its Impact on [Measures of] Metabolic Tumor Volume

Knaup,

Weindler,

van Heek

et al. 2024

Academic Radiology

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Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine

Wendler,

Kreissl,

Schemmer

et al. 2023

Nuklearmedizin

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Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.

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Prognostic value of baseline metabolic tumor volume (MTV) for forecasting chemotherapy outcome in early‐stage unfavorable Hodgkin lymphoma: Data from the phase III HD17 trial

Cited by 3 publications

References 34 publications

How to treat localized Hodgkin lymphoma?

How to treat localized Hodgkin lymphoma?

PET/CT Reconstruction and Its Impact on [Measures of] Metabolic Tumor Volume

Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine

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