Breast Cancer Patients' Depression Prediction by Machine Learning Approach

Cvetković, Jovana

doi:10.1080/07357907.2017.1363892

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…Of the 42 included studies, 10 unique symptoms were reported as outcome variables in the predictions. Those included were xerostomia (9/42, 14%) [ 27 , 30 , 34 , 49 - 51 , 55 , 58 , 60 ], depression (8/42, 13%) [ 22 , 33 , 37 , 41 , 43 , 45 , 52 , 59 ], pain (8/42, 13%) [ 20 , 25 , 26 , 35 , 37 , 40 , 42 , 56 ], fatigue (6/42, 10%) [ 23 , 24 , 29 , 38 , 42 , 44 ], anxiety (3/42, 5%) [ 33 , 46 , 52 ], sleep disturbance or insomnia (3/42, 5%) [ 26 , 28 , 52 ], nausea or vomiting (3/42, 5%) [ 17 , 26 , 29 ], weight loss (2/42, 3%) [ 47 , 53 ], cognitive impairment (2/42, 3%) [ 21 , 36 ], and diarrhea (2/42, 3%) [ 29 , 42 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

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Background People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. Objective This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. Methods We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. Results A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods—deep learning, ensemble classifiers, and unsupervised ML—constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. Conclusions This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Zeinali,

Youn,

Albashayreh

et al. 2024

JMIR Cancer

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“…Clinically relevant predictive performance of common ML classification algorithms was shown in two studies predicting postpartum depression [ 58 , 59 ]. Cvetkovic [ 60 ] used a deep-learning approach to predict depression in breast cancer patients, achieving high internal accuracy. However, the study methodology was poorly reported, with information lacking on data preprocessing and model testing [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…Cvetkovic [ 60 ] used a deep-learning approach to predict depression in breast cancer patients, achieving high internal accuracy. However, the study methodology was poorly reported, with information lacking on data preprocessing and model testing [ 60 ]. In another study, depression and anxiety in college students were estimated using GBM, with satisfactory performance yielding an AUC of 0.730 [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

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Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study

Nowinka¹,

Alagha²,

Mahmoud³

et al. 2022

JMIR Form Res

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Background Knee osteoarthritis (OA) is the most common form of OA and a leading cause of disability worldwide. Chronic pain and functional loss secondary to knee OA put patients at risk of developing depression, which can also impair their treatment response. However, no tools exist to assist clinicians in identifying patients at risk. Machine learning (ML) predictive models may offer a solution. We investigated whether ML models could predict the development of depression in patients with knee OA and examined which features are the most predictive. Objective The primary aim of this study was to develop and test an ML model to predict depression in patients with knee OA at 2 years and to validate the models using an external data set. The secondary aim was to identify the most important predictive features used by the ML algorithms. Methods Osteoarthritis Initiative Study (OAI) data were used for model development and external validation was performed using Multicenter Osteoarthritis Study (MOST) data. Forty-two features were selected, which denoted routinely collected demographic and clinical data such as patient demographics, past medical history, knee OA history, baseline examination findings, and patient-reported outcome measures. Six different ML classification models were trained (logistic regression, least absolute shrinkage and selection operator [LASSO], ridge regression, decision tree, random forest, and gradient boosting machine). The primary outcome was to predict depression at 2 years following study enrollment. The presence of depression was defined using the Center for Epidemiological Studies Depression Scale. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. The most important features were extracted from the best-performing model on external validation. Results A total of 5947 patients were included in this study, with 2969 in the training set, 742 in the test set, and 2236 in the external validation set. For the test set, the AUC ranged from 0.673 (95% CI 0.604-0.742) to 0.869 (95% CI 0.824-0.913), with an F1 score of 0.435 to 0.490. On external validation, the AUC varied from 0.720 (95% CI 0.685-0.755) to 0.876 (95% CI 0.853-0.899), with an F1 score of 0.456 to 0.563. LASSO modeling offered the highest predictive performance. Blood pressure, baseline depression score, knee pain and stiffness, and quality of life were the most predictive features. Conclusions To our knowledge, this is the first study to apply ML classification models to predict depression in patients with knee OA. Our study showed that ML models can deliver a clinically acceptable level of performance (AUC>0.7) in predicting the development of depression using routinely available demographic and clinical data. Further work is required to address the class imbalance in the training data and to evaluate the clinical utility of the models in facilitating early intervention and improved outcomes.

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“…Full‐field OCT and full‐field optical coherence elastography have been also employed for breast cancer margin assessment . Machine learning model has been established for the automatic quantification of breast tissues using OCT systems . The previous studies for the assessment of breast cancer margin involve various features extracted from either OCT A‐line features or features extracted from OCT B‐scan images (texture analysis, fractal analysis) .…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network

Singla

Dubey

Srivastava

2018

Journal of Biophotonics

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The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)‐stained tissue biopsy. Resurgery is required in 20% to 30% of cases because of incomplete excision of malignant tissues. Therefore, a more accurate method is required to detect the cancer margin to avoid the risk of recurrence. In the recent years, convolutional neural networks (CNNs) has achieved excellent performance in the field of medical images diagnosis. It automatically extracts the features from the images and classifies them. In the proposed study, we apply a pretrained Inception‐v3 CNN with reverse active learning for the classification of healthy and malignancy breast tissue using optical coherence tomography (OCT) images. This proposed method attained the sensitivity, specificity and accuracy is 90.2%, 91.7% and 90%, respectively, with testing datasets collected from 48 patients (22 normal fibro‐adipose tissue and 26 Invasive ductal carcinomas cancerous tissues). The trained network utilizes for the breast cancer margin assessment to predict the tumor with negative margins. Additionally, the network output is correlated with the corresponding histology image. Our results lay the foundation for the future that the proposed method can be used to perform automatic intraoperative identification of breast cancer margins in real‐time and to guide core needle biopsies.

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Breast Cancer Patients' Depression Prediction by Machine Learning Approach

Cited by 17 publications

References 11 publications

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network

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