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Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

ABSTRACT: Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

SUBMITTER: Korte JC

PROVIDER: S-EPMC8417253 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

Korte James C JC Cardenas Carlos C Hardcastle Nicholas N Kron Tomas T Wang Jihong J Bahig Houda H Elgohari Baher B Ger Rachel R Court Laurence L Fuller Clifton D CD Ng Sweet Ping SP

Scientific reports 20210903 1

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant softwa ...[more]

PMID: 34480036

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Project description:PurposeTo determine the feasibility of texture analysis of apparent diffusion coefficient (ADC) maps and to assess the performance of texture analysis and ADC to predict histologic grade, parametrial invasion, lymph node metastasis, International Federation of Gynecology and Obstetrics (FIGO) stage, recurrence, and recurrence-free survival (RFS) in patients with cervical carcinoma.Materials and methodsThis retrospective study included 58 patients with cervical carcinoma who were examined with a 1.5-T MRI system and diffusion-weighted imaging with b values of 0 and 1000 sec/mm2. Software with volumes of interest on ADC maps was used to extract 45 texture features, including higher-order texture features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance of ADC map random forest models and of ADC values. Dunnett test, Spearman rank correlation coefficient, Kaplan-Meier analyses, log-rank test, and Cox proportional hazards regression analyses were also used for statistical analyses.ResultsThe ADC map random forest models showed a significantly larger area under the ROC curve (AUC) than the AUC of ADC values for predicting high-grade cervical carcinoma (P = .0036), but not for parametrial invasion, lymph node metastasis, stages III-IV, and recurrence (P = .0602, .3176, .0924, and .5633, respectively). The random forest models predicted that the mean RFS rates were significantly shorter for high-grade cervical carcinomas, parametrial invasion, lymph node metastasis, stages III-IV, and recurrence (P = .0405, < .0001, .0344, .0001, and .0015, respectively); the random forest models for parametrial invasion and stages III-IV were more useful than ADC values (P = .0018) for predicting RFS.ConclusionThe ADC map random forest models were more useful for noninvasively evaluating histologic grade, parametrial invasion, lymph node metastasis, FIGO stage, and recurrence and for predicting RFS in patients with cervical carcinoma than were ADC values.Keywords: Comparative Studies, Genital/Reproductive, MR-Diffusion Weighted Imaging, MR-Imaging, Neoplasms-Primary, Pathology, Pelvis, Tissue Characterization, UterusSupplemental material is available for this article.© RSNA, 2020See also the commentary by Reinhold and Nougaret in this issue.

Project description:BackgroundBrain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC.MethodsWe enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere.ResultsOut of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988.ConclusionsIn patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer.Trial registrationThe study was registered in the German Clinical Trials Register (DRKS00023016).

Project description:Background and Purpose- In this era of endovascular therapy (EVT) with early, complete recanalization and reperfusion, we have observed an even more rapid apparent diffusion coefficient (ADC) normalization within the acute ischemic lesion compared with the natural history or IV-tPA-treated patient. In this study, we aimed to evaluate the effect of revascularization on ADC evolution within the core lesion in the first 24 hours in acute ischemic stroke patients. Methods- This retrospective study included anterior circulation acute ischemic stroke patients treated with EVT with or without intravenous tPA (IVT) from 2015 to 2017 compared with a consecutive cohort of IVT-only patients treated before 2015. Diffusion-weighted imaging and ADC maps were used to quantify baseline core lesions. Median ADC value change and core reversal were determined at 24 hours. Diffusion-weighted imaging lesion growth was measured at 24 hours and 5 days. Good clinical outcome was defined as modified Rankin Scale score of 0 to 2 at 90 days. Results- Twenty-five patients (50%) received IVT while the other 25 patients received EVT (50%) with or without IVT. Between these patient groups, there were no differences in age, sex, baseline National Institutes of Health Stroke Scale, interhospital transfer, or IVT rates. Thirty-two patients (64%) revascularized with 69% receiving EVT. There was a significant increase in median ADC value of the core lesion at 24 hours in patients who revascularized compared with further ADC reduction in nonrevascularization patients. Revascularization patients had a significantly higher rate of good clinical outcome at 90 days, 63% versus 9% (P=0.003). Core reversal at 24 hours was significantly higher in revascularization patients, 69% versus 22% (P=0.002). Conclusions- ADC evolution in acute ischemic stroke patients with early, complete revascularization, now more commonly seen with EVT, is strikingly different from our historical understanding. The early ADC normalization we have observed in this setting may include a component of secondary injury and serve as a potential imaging biomarker for the development of future adjunctive therapies. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT00009243.

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Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

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Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

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