Dataset Information

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).

ABSTRACT:

Purpose

A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies.

Methods

The NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model.

Results

The proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies.

Conclusion

The proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.

SUBMITTER: Moitra D

PROVIDER: S-EPMC6667593 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).

Moitra Dipanjan D Mandal Rakesh Kr RK

Health information science and systems 20190730 1

<h4>Purpose</h4>A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the trad ...[more]

PMID: 31406570

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Project description:ImportanceTo our knowledge, there are no validation studies to date of the prognostic value of the AJCC Cancer Staging Manual, eighth edition (AJCC 8), criteria for eyelid and periocular squamous cell carcinoma.ObjectiveTo determine the association of tumor (T) category in AJCC 8 with local recurrence, nodal metastasis, distant metastasis, and disease-specific survival (DSS) for eyelid and periocular squamous cell carcinoma.Design, setting, and participantsIn this retrospective, single-center cohort study, 109 consecutive patients with eyelid and periocular squamous cell carcinoma treated from January 1999 to April 2018 were included. Patients with secondary involvement of the periocular region were excluded.Main outcomes and measuresLocal recurrence, nodal metastasis, distance metastasis, and DSS.ResultsOf the 109 included patients, 81 (74.3%) were male, and the median (range) age was 66 (40-91) years. At presentation, 43 patients (39.4%) had recurrent tumor, 4 (3.7%) had nodal metastasis, and 1 (0.9%) had distant metastasis. The median (range) follow-up was 23 (1-161) months. During follow-up, 11 patients (10.1%) developed local recurrence, 7 (6.4%) developed nodal metastasis, 2 (1.8%) developed distant metastasis, and 9 (8.3%) died of disease. The 5-year DSS rate was 87.7% (95% CI, 79.5-96.9). Chronic immunosuppression (hazard ratio, 47.24; 95% CI, 7.33-304.30; P < .001) and presentation with recurrent squamous cell carcinoma (hazard ratio, 5.22; 95% CI, 1.12-24.31; P = .04) were associated with local recurrence during follow-up. Of the 11 patients with local recurrence during follow-up, 7 (64%) had perineural invasion. T category was associated with nodal metastasis; clinical stage of T2c or worse at presentation was associated with higher risk of nodal metastasis and death of disease but not with a higher risk of local recurrence. Distant metastasis was associated with nodal metastasis at presentation (hazard ratio, 32.50; 95% CI, 1.97-536.40; P = .02) and during follow-up. A total of 33 patients (30.3%) had different T categories depending on whether disease was staged according to the seventh or eighth edition of the AJCC Cancer Staging Manual. Compared with AJCC 7, AJCC 8 showed a better predictive value in terms of local recurrence (T3, 17% vs 14%; T4, 11% vs 16%) and DSS.Conclusions and relevanceThese findings suggest that T category in AJCC 8 is associated with nodal metastasis and DSS. Immunosuppression and presentation with recurrent disease are associated with increased risk of future local recurrence. Patients with tumors of clinical stage T2c or worse at presentation are at increased risk of nodal metastasis and worse DSS and should undergo surveillance for nodal metastasis. Future studies, ideally prospective in design, could provide greater confidence in these findings.

Dataset Information

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).

Purpose

Methods

Results

Conclusion

Publications

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).

Similar Datasets

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