Imaging Pearls ❯ Adrenal ❯ Artificial Intelligence
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- Objectives: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions.
Materials and methods: This retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation. DL models based on multiphase CT images were developed, validated and tested. The diagnostic performance of the classification models, imaging phases and radiologists with or without DL were compared using accuracy (ACC) and receiver operating characteristic (ROC) curves. Integrated discrimination improvement (IDI) analysis and the DeLong test were used to compare the area under the curve (AUC) among models. Decision curve analysis (DCA) was used to assess the clinical usefulness of the predictive models
Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists
Changyi Ma et al.
European Journal of Radiology 169 (2023) 111169 - Results: The DL signature based on LASSO (DLSL) had a higher AUC than that of the other classification models (IDI > 0, P < 0.05). Furthermore, the precontrast phase (PCP)-based DLSL performed best in the independent external validation (AUC = 0.881, ACC = 82.9 %) and clinical test cohorts (AUC = 0.790, ACC = 70.4 %), outperforming DLSL based on the other single-phase or three-phase images (IDI > 0, P < 0.05). DCA demonstrated that PCP-based DLSL provided a higher net benefit (0.01–0.95).
Conclusion: The DL model based on PCP CT images performed acceptably in differentiating adrenal metastases from benign lesions, and it may assist radiologists in accurate tumor staging for patients with a history of extra-adrenal malignancy.
Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists
Changyi Ma et al.
European Journal of Radiology 169 (2023) 111169 - In this study, we developed a DL model based on CT images that can effectively differentiate adrenal metastases from benign lesions in patients with a history of extra-adrenal malignancies. We found that the DLSL model based on PCP exhibited a comparable diagnostic performance compared to each single- or three-phase CT image and could help radiologists perform accurate imaging staging for patients with a known extra-adrenal cancer history. To our knowledge, this is the first time that a DL model has been used to differentiate adrenal metastases from benign lesions in a large population.
Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists
Changyi Ma et al.
European Journal of Radiology 169 (2023) 111169 - In the comparison with radiologists, DLSL showed a diagnostic performance comparable to that of radiologists with moderate years of experience. It had good supplementary value for improving the diagnostic performance of each radiologist, especially those with less experience. Furthermore, we found that DLSL significantly improved the specificity of the radiologists. This may be because, in the first retrospective CT evaluation, the radiologists were more influenced by the patient’s cancer history in their clinical diagnostic experience[3], which may have led to an increase in subjectivity in diagnosing cases of metastases. In the second retrospective CT evaluation, the radiologists leveraged the DLSL predictions for ambiguous cases to make the final decision, thus improving the accuracy of diagnosis.
Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists
Changyi Ma et al.
European Journal of Radiology 169 (2023) 111169
- “AI has currently applications in almost all fields of adrenal diseases. Although most studies are preliminary studies, they suggest that AI may improve AL classification, prognosis, and possibly management of patients affected with AL. However, there is a long way to go before AI is implemented into daily practice of radiologist, endocrinologist, and surgeons. In this regard, one current limitation for immediate applicability is the observed difference in recommended management among societies. Some studies suggest that AI algorithms should not be built using imaging data alone but should integrate biological data for better efficacy. Finally, large, prospective studies with external validations are needed to build effective, predictive models that will help improve patients’ care by selecting the most appropriate option among surveillance, open vs. laparoscopic surgery or “leave it alone” option.”
Artificial intelligence in adrenal imaging: A critical review of currentapplications
Maxime Barata, Martin Gaillard, Anne-S egol ene Cottereaub, Elliot K. Fishman, Guillaume Assi, Anne Jouinotb, Christine Hoeffelg, Philippe Soyera, Anthony Dohana,
Diagnostic and Interventional Imaging 104 (2023) 37−42 - “Three studies haved investigated the capabilities of AI using CT data. Kusunoki et al. evaluated a complex algorithm using deep convolutional neuronal network to identify AA among undetermined AL on CT. Using a sample of 112 ALs, they found 87% sensitivity (95% CI: 80−93%), 96% specificity (95% CI: 88−100%) and an AUC of 0.94 (95% CI: 0.86−0.99%) for the best model for the diagnosis of AA vs. other conditions . Considering large ALs (i.e., > 4 cm), a radiomics-based ML algorithm was evaluated to differentiate ACC from AA using CT data . The algorithm yield 82% accuracy (95% CI: 69−92%) for differentiating between the two entities in a study population of 29 patients with ACC and 25 with AA). Of interest, the algorithm performed significantly better that the radiologist alone (69% accuracy; P < 0.0001) . However, because of small cohorts and lack of external validation, these two studies should be considered as preliminary/feasibility studies only.”
Artificial intelligence in adrenal imaging: A critical review of currentapplications
Maxime Barata, Martin Gaillard, Anne-S egol ene Cottereaub, Elliot K. Fishman, Guillaume Assi, Anne Jouinotb, Christine Hoeffelg, Philippe Soyera, Anthony Dohana,
Diagnostic and Interventional Imaging 104 (2023) 37−42 - “Recently, deep neuronal networks (DNN) were developed to improve accuracy of adrenal gland segmentation. Luo et al. used a two-step method with a preprocessing step to reduce variabilities between examinations and the computational burden followed by a second step of small organs segmentation network using annotated input. The CT dataset was obtained from 348 patients, 60% for the training, 20% for the validation and 20% for the testing cohort . With this model, the authors obtained a DSC of 87.2%, which is the best reported one to date for adrenal gland segmentation .Despite a better DSC than those obtained with previous segmentation algorithms, DNNs also require further external validation to demonstrate their robustness.”
Artificial intelligence in adrenal imaging: A critical review of currentapplications
Maxime Barata, Martin Gaillard, Anne-S egol ene Cottereaub, Elliot K. Fishman, Guillaume Assi, Anne Jouinotb, Christine Hoeffelg, Philippe Soyera, Anthony Dohana,
Diagnostic and Interventional Imaging 104 (2023) 37−42
- “In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.”
Artificial intelligence in adrenal imaging: A critical review of current applications
Maxime Baratab, Martin Gaillardb, Anne-Segolene Cottereaub, Elliot K. Fishman, Guillaume Assieb, Anne Jouinotb, Christine Hoeffel, Philippe Soyer, Anthony Dohana
Diagnostic and Interventional Imaging 000 (2022) 1−6 - "Recently, deep neuronal networks (DNN) were developed to improve accuracy of adrenal gland segmentation. Luo et al. used a two-step method with a preprocessing step to reduce variabilities between examinations and the computational burden followed by a second step of small organs segmentation network using annotated input . The CT dataset was obtained from 348 patients, 60% for the training, 20% for the validation and 20% for the testing cohort . With this model, the authors obtained a DSC of 87.2%, which is the best reported one to date for adrenal gland segmentation Despite a better DSC than those obtained with previous segmentation algorithms, DNNs also require further external validation to demonstrate their robustness.”
Artificial intelligence in adrenal imaging: A critical review of current applications
Maxime Baratab, Martin Gaillardb, Anne-Segolene Cottereaub, Elliot K. Fishman, Guillaume Assieb, Anne Jouinotb, Christine Hoeffel, Philippe Soyer, Anthony Dohana
Diagnostic and Interventional Imaging 000 (2022) 1−6 - "Radiomics is regarded as a promising research field for the characterization and follow-up of non-typical AL. Using first order texture analysis features, Jhaveri et al. found that a 5% cut-off of negative voxels on histogram derived from unenhanced CT data had 92.3% sensitivity and 100% specificity for the characterization of lipid-poor AA . Another found that entropy (i.e., a first order texture analysis feature) yielded an AUC of 0.65 (95% CI: 0.52−0.77) to differentiate between adrenal metastasis from lung cancer (showing greater entropy) and AA Ho et al. developed a model using 21 second order features extracted from contrast-enhanced CT data that allowed differentiating lipid-poor AA from malignant AL better than standard morphological features (AUC of 0.8 vs. 0.6, respectively). Similarly, a model using four second-order features extracted from unenhanced CT data yielded 96.6% sensitivity, 81% specificity and 85.2% accuracy for the diagnosis of pheochromocytoma vs. lipid poor AA.”
Artificial intelligence in adrenal imaging: A critical review of current applications
Maxime Baratab, Martin Gaillardb, Anne-Segolene Cottereaub, Elliot K. Fishman, Guillaume Assieb, Anne Jouinotb, Christine Hoeffel, Philippe Soyer, Anthony Dohana
Diagnostic and Interventional Imaging 000 (2022) 1−6 - “AI has currently applications in almost all fields of adrenal diseases. Although most studies are preliminary studies, they suggestthat AI may improve AL classification, prognosis, and possibly management of patients affected with AL. However, there is a long way to go before AI is implemented into daily practice of radiologist, endocrinologist, and surgeons. In this regard, one current limitation for immediate applicability is the observed difference in recommended management among societies. Some studies suggest that AI algorithms should not be built using imaging data alone but should integrate biological data for better efficacy. Finally, large, prospective studies with external validations are needed to buildeffective, predictive models that will help improve patients’ care by selecting the most appropriate option among surveillance, open vs. laparoscopic surgery or “leave it alone” option.”
Artificial intelligence in adrenal imaging: A critical review of current applications
Maxime Baratab, Martin Gaillardb, Anne-Segolene Cottereaub, Elliot K. Fishman, Guillaume Assieb, Anne Jouinotb, Christine Hoeffel, Philippe Soyer, Anthony Dohana
Diagnostic and Interventional Imaging 000 (2022) 1−6 - “A machine learning algorithm was developed that can accurately segment and classify adrenal glands as normal or mass-containing on contrastvenhanced CT images, with performance similar to that of radiologists.”
Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
Cory Robinson-Weiss et al.
Radiology 2022; 000:1–8 - • In this retrospective study using CT images from 251 (development data set) and 991 patients (secondary test set), a machine learning algorithm segmented adrenal glands, with a performance similar to that of radiologists; the median model Dice score 0.87 versus 0.89 for normal adrenal glands and 0.85 versus 0.89 for adrenal masses.
• The algorithm differentiated adrenal masses from normal adrenal glands, with 83% sensitivity and 89% specificity for the development data set and 69% sensitivity and 91% specificity for the secondary test set.
Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
Cory Robinson-Weiss et al.
Radiology 2022; 000:1–8 - “ Our results are promising given that the adrenal gland is an inherently challenging organ to segment compared with larger organs (such as the liver and kidneys), as these glands are small and change in position due to respiration, and their shape, size, and location can vary by laterality and patient. In addition, adrenal glands have soft-tissue attenuation at CT that is similar to that of adjacent structures, including the liver, pancreas, kidneys, and vasculature. In patients with a paucity of intra-abdominal fat, the adrenal glands may be even more challenging to delineate from adjacent structures without the contrasting fat around the glands to separate them from other structures. Finally, there may be external mass effect on the glands, for example from an adjacent liver cyst or mass.”
Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
Cory Robinson-Weiss et al.
Radiology 2022; 000:1–8 - “In conclusion, we propose a two-stage machine learning pipeline to automatically segment the adrenal glands at contrast enhanced CT and then classify the glands as normal or mass containing. This tool may be used to assist radiologists in accurate and expedient image interpretation and potentially decrease interreader variability. Future work is needed to improve the classification stage of our model, as well as expand on the scope of the classification task by reviewing prior imaging and assessing for mass stability or growth.”
Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
Cory Robinson-Weiss et al.
Radiology 2022; 000:1–8 - “In conclusion, only one-fourth of patients received appropriate biochemical testing after incidental diagnosis of an adrenal nodule, and most nodules with indeterminate imaging characteristics did not have follow-up imaging. Advanced data analytic techniques on electronic imaging reports may aid in the clinical identification andimproved management of patients with adrenal incidentalomas.”
Automated extraction of incidental adrenal nodules from electronic health records
Max Schumm et al.
Surgery xxx (2022) 1e7 (in press) - Background: Many adrenal incidentalomas do not undergo appropriate biochemical testing and complete imaging characterization to assess for hormone hypersecretion and malignancy. With the growing availability of clinical narratives in the electronic medical record, automated surveillance using advanced data analytic techniques may represent a promising method to improve management.
Conclusion: Only one-fourth of patients received appropriate biochemical testing after incidental diagnosis of an adrenal nodule, and most nodules with indeterminate imaging characteristics did notundergo follow-up imaging. Advanced data analytic techniques on electronic imaging reports may aid in the clinical identification and improved management of patients with adrenal incidentalomas.
Automated extraction of incidental adrenal nodules from electronic health records
Max Schumm et al.
Surgery xxx (2022) 1e7 (in press)
- "The approach for adrenal incidentalomas could also benefit from AI-based imaging analysis. Adrenal nodules are the most frequently encountered incidental radiographic finding and can reflect malignant (ie, pheochromocytoma) or benign (ie, adenoma) conditions with overlapping imaging characteristics Quantitative texture analysis through high-throughput extraction might differentiate radiographic adrenal lesions into discrete clinical subsets, reducing costly and invasive testing.”
Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints
Ohad Oren, Bernard J Gersh, Deepak L Bhatt
Lancet Digital Health Vol 2 September 2020 (in press) - “The rise and dissemination of AI in clinical medicine will refine our diagnostic accuracy and rule-out capabilities. However, unless AI algorithms are trained to distinguish between benign abnormalities and clinically meaningful lesions, better imaging sensitivity might come at the cost of increased false positives, as well as perplexing scenarios whereby AI findings are not associated with outcomes. To facilitate the study of AI in medical image interpretation, it is paramount to assess the effects on clinically meaningful endpoints to improve applicability and allow effective deployment into clinical practice.”
Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints
Ohad Oren, Bernard J Gersh, Deepak L Bhatt
Lancet Digital Health Vol 2 September 2020 (in press)