Automated histologic diagnosis of CNS tumors with machine learning

Khalsa, Siri Sahib S.; Hollon, Todd C.; Adapa, Arjun; Urias, Esteban; Srinivasan, Saumini; Jairath, Neil; Szczepanski, Julianne M; Ouillette, Peter; Camelo-Piragua, Sandra; Orringer, Daniel

doi:10.2217/cns-2020-0003

Cited by 24 publications

(23 citation statements)

References 45 publications

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“…AI algorithms are effective in detecting these minute differences for accurate diagnosis. 26,56 In a study by Krivoshapkin et al (2016), MRI histogram peaks were used to formulate the AI algorithm to study the tumor volumes. By Tukey test and the Games-Howell test, the authors identified the mean deviation in agreement index between specialists (neurosurgeons and radiologists) was 0.98 ± 0.007 (±SEM).…”

Section: Preoperative Diagnosismentioning

confidence: 99%

“…Various authors mention that by deploying computer algorithms, the accuracy of diagnosis and inter-operator repeatability can be improved. 26,56 While many factors make the neurosurgical procedure successful, the surgeon's planning and competency are integral. AI algorithms have proven helpful in these aspects by improving anatomical delineation and enriching simulation-based training.…”

Section: Summary Of the Contentmentioning

confidence: 99%

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Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Agadi¹,

Dominari²,

Tebha³

et al. 2023

J Korean Neurosurg Soc

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Central nervous system (CNS) tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive.Artificial intelligence, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

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Section: Preoperative Diagnosismentioning

confidence: 99%

Section: Summary Of the Contentmentioning

confidence: 99%

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Agadi¹,

Dominari²,

Tebha³

et al. 2023

J Korean Neurosurg Soc

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“…This may aid surgeons in making more accurate surgical decisions to avoid unnecessary lung function loss and related complications. Future studies should consider using deep learning to quantitatively analyze paraffin sections (used to determine neurological tumor pathology) (37) and intraoperative FS images to incorporate them into the model, improving the model accuracy and increasing the objectivity of intraoperative FS analysis.…”

Section: Study Limitationsmentioning

confidence: 99%

A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules

Qian

Zhou

Zeng

et al. 2022

Transl Lung Cancer Res

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Background: Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals.Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection.Methods: Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them. Results:The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort.The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort.Conclusions: Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low-and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.

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“…7,33,34 The large data sets generated from SRH can lend themselves to ML approaches to rapidly classify tissue. Orringer et al, have developed ML approaches to enable automated diagnosis of CNS tumours from their SRH imagesas reviewed by Khalsa et al 35 Their approach has evolved from using vector-based multilayer perceptron (MLP) which are trained using specialised knowledge of CNS tumours to manually define important features (nuclear morphology, cell density, vascularity, etc.) and can result in biasing of data, 10 to using deep CNN that retain spatial information and learn what image features are important in determining diagnosis and predicting patient outcomes.…”

Section: Computer Aided Diagnosismentioning

confidence: 99%