Prognostic value of tumor volume in patients with head and neck squamous cell carcinoma treated with primary surgery

Dejaco, Daniel; Steinbichler, Teresa Bernadette; Schartinger, Volker Hans; Fischer, Natalie; Anegg, Maria; Dudás, József; Posch, Andrea; Widmann, Gerlig; Riechelmann, Herbert

doi:10.1002/hed.25040

Cited by 21 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In planning CTs of the same patients 3–4 weeks later, mean PT-volume was approximately 25 mL (median≈17 mL; table 2). These PT-volumes are in line with previous publications reporting mean volumes of 11–37 mL 23–26. Mean LN-volume measured in diagnostic CTs was 9 mL (median≈2 mL) and approximately 15 mL (median≈4 mL) in planning CTs (table 2).…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

Specific growth rates calculated from CTs in patients with head and neck squamous cell carcinoma: a retrospective study performed in Austria

et al. 2019

Self Cite

View full text Add to dashboard Cite

ObjectiveTo provide data on specific growth rates (SGRs) of primary tumours (PT-SGR) and largest pathological cervical lymph nodes (LN-SGR) for head and neck squamous cell carcinoma (HNSCC). To explore PT-SGR’s and LN-SGR’s correlation with selected biomarkers epidermal growth factor receptor (EGFR), Ki67 and CD44.Design and settingRetrospective study performed at a tertiary oncological referral centre in Innsbruck, Austria.ParticipantsAdult patients with incident HNSCC treated with primary radiotherapy (RT) or radiochemotherapy (RCT).Outcome measuresVolumes of the primary tumour (PT-volume) and largest pathological cervical lymph node (LN-volume) were measured in CT scans obtained at time of diagnosis and subsequent planning CTs immediately prior to RT or RCT. SGRs were calculated assuming an exponential growth function. PT-SGR’s and LN-SGR’s correlation with EGFR, Ki67 and CD44 were explored.ResultsIn 123 patients, mean interval between diagnostic and planning CT was 29±21 days. PT-SGR was 1.8±1.8% (mean±SD) per day and was positively correlated with EGFR, Ki67 and CD44 expression (p=0.02; p=0.02; p=0.03). LN-SGR was 1.7±2.0% per day and increased with larger initial LN-volume, was lower in laryngeal cancer (p=0.003) and slowed down with time. LN-SGR was not correlated with EGFR, Ki67 or CD44 expression in primary tumours (p>0.12). New cartilage or bone infiltration occurred in 10 patients and new central lymph node necrosis in 8 patients.ConclusionsHNSCCs are fast-growing tumours for which treatment must not be delayed. Clinical tumour growth rates are influences by EGFR, KI67 and CD44 expression.

show abstract

Section: Discussionsupporting

confidence: 91%

“…The development of the research question was based on previous publications exploring tumour volume in HNSCC15 and its prognostic value if treated primarily with surgery 23. Neither patients nor the public were involved in the design of the study, the recruitment of the study or the conduct of the study.…”

Section: Methodsmentioning

confidence: 99%

Specific growth rates calculated from CTs in patients with head and neck squamous cell carcinoma: a retrospective study performed in Austria

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tumour volume is a well-established and benchmarked prognostic factor for lung and head and neck cancers [5,11]. Therefore, correctly identifying if a feature or a combination of feature (e.g.…”

Section: Discussionmentioning

confidence: 99%

Machine learning helps identifying volume-confounding effects in radiomics

et al. 2020

View full text Add to dashboard Cite

Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML)based methods for robust radiomics signatures development.

show abstract

“…Knegjens studied 360 patients undergoing primary chemoradiotherapy for head and neck cancer and found a 14% increased hazard ratio for local recurrence per 10 cm 3 increase in TV 37 . Dejaco et al measured TV in 125 patients undergoing surgical resection for head and neck cancer, and found that volume was predictive of worse OS 28 …”

Section: Discussionmentioning

confidence: 99%

“…The prognostic value of primary tumor volume (TV) in predicting disease progression and survival has been reported in other head and neck cancers, 28 but not in SNMM. Our objective was to define the relationship between TV and the risk of distant metastases and OS in patients with SNMM.…”

Section: Introductionmentioning

confidence: 99%

Primary tumor volume as a predictor of distant metastases and survival in patients with sinonasal mucosal melanoma

et al. 2020

View full text Add to dashboard Cite

Background: Sinonasal mucosal melanoma (SNMM) is an aggressive cancer with high mortality. Identifying patients at risk of distant metastasis assists with management and prognostication. We aimed to define the relationship between volume, survival, and risk of distant metastases. Methods: A retrospective review of all patients with SNMM treated at a single institution over a 21-year period was conducted. Tumor volume was calculated using cross-sectional imaging and survival analysis was performed. Results: Sixty-one patients were included. Tumor volume was predictive of local progression-free survival (P = .03), distant metastases-free survival (DMFS) (P = .002), and overall survival (OS) (P = .02). It was a better predictor than AJCC stage and T-classification. Tumor volume equal to or greater than 5 cm 3 was associated with a significantly worse DMFS and OS (P = .02 and .009, respectively). Conclusion: Calculation of tumor volume assists in quantifying the risk of distant metastases and death in SNMM.

show abstract

Prognostic value of tumor volume in patients with head and neck squamous cell carcinoma treated with primary surgery

Abstract: The tumor volume had an impact on CR, OS, and clear margins. The tumor volume may also aid in selecting HNSCC treatment.

Cited by 21 publications

References 22 publications

Specific growth rates calculated from CTs in patients with head and neck squamous cell carcinoma: a retrospective study performed in Austria

Specific growth rates calculated from CTs in patients with head and neck squamous cell carcinoma: a retrospective study performed in Austria

Machine learning helps identifying volume-confounding effects in radiomics

Primary tumor volume as a predictor of distant metastases and survival in patients with sinonasal mucosal melanoma

Contact Info

Product

Resources

About