&lt;p&gt;Progress of Artificial Intelligence in Gynecological Malignant Tumors&lt;/p&gt;

Jie, Zhou; Zeng, Zhi; Li, Li

doi:10.2147/cmar.s279990

Cited by 16 publications

(13 citation statements)

References 67 publications

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“…The evolution of ML technology in the field of gynaecological oncology has been described. 10 We previously demonstrated the feasibility of using a ML approach, the k-NN model, which is very much reflective of ‘previous clinical experience’ for accurate prediction of complete cytoreduction in advanced-stage HGSOC surgery. 11…”

Section: Introductionmentioning

confidence: 93%

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning

et al. 2021

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Introduction Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We designed a study to support the feature selection for the 2-year prognostic period and compared the performance of several Machine Learning prediction algorithms for accurate 2-year prognosis estimation in advanced-stage high grade serous ovarian cancer (HGSOC) patients. Methods The prognosis estimation was formulated as a binary classification problem. Dataset was split into training and test cohorts with repeated random sampling until there was no significant difference (p = 0.20) between the two cohorts. A ten-fold cross-validation was applied. Various state-of-the-art supervised classifiers were used. For feature selection, in addition to the exhaustive search for the best combination of features, we used the-chi square test of independence and the MRMR method. Results Two hundred nine patients were identified. The model's mean prediction accuracy reached 73%. We demonstrated that Support-Vector-Machine and Ensemble Subspace Discriminant algorithms outperformed Logistic Regression in accuracy indices. The probability of achieving a cancer-free state was maximised with a combination of primary cytoreduction, good performance status and maximal surgical effort (AUC 0.63). Standard chemotherapy, performance status, tumour load and residual disease were consistently predictive of the mid-term overall survival (AUC 0.63–0.66). The model recall and precision were greater than 80%. Conclusion Machine Learning appears to be promising for accurate prognosis estimation. Appropriate feature selection is required when building an HGSOC model for 2-year prognosis prediction. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC 2-year prognosis.

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Section: Introductionmentioning

confidence: 93%

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning

et al. 2021

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“…Several CNNs using MRI have been constructed to diagnose uterine tumors to date [ 20 , 21 ]. Urushibara et al recently developed a CNN that can differentiate between cervical cancer and non-cancerous lesions on T2WI [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists

et al. 2022

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Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. Results The AUC of the CNNs pertaining to the single and combined image sets were 0.88–0.95 and 0.87–0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88–0.95. Conclusion CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.

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“…Several CNNs using MRI have been constructed for the diagnosis of uterine tumors to date (19,20). (22), and T2WI + CE-T1WI (23).…”

Section: Discussionmentioning

confidence: 99%

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: A comparison with radiologists

Urushibara

Mori

Ishiguro

et al. 2022

Preprint

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Purpose: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using single image sets improved the diagnostic performance of CNNs. Results: The AUC of the CNNs pertaining to single and combined image sets were 0.88–0.95 and 0.87-0.93, respectively, indicating better or equivalent diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of images was 0.88–0.95. Conclusion: CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some image sets.

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<p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>

Cited by 16 publications

References 67 publications

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: A comparison with radiologists

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