Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence

Tasoulis, Dimitris K.; Spyridonos, Panagiota; Pavlidis, Nicos G.; Plagianakos, Vassilis P.; Ravazoula, Panagiota; Nikiforidis, Georgios; Vrahatis, Michael N.

doi:10.1016/j.artmed.2006.07.008

Cited by 6 publications

(6 citation statements)

References 46 publications

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“…The existing prediction methods rely on clinicopathological data, which are often insufficient, thus emphasizing the need for an affordable, rapid, reproducible, and effective technique. CPATH has the potential to predict clinical outcomes by analyzing tumor growth patterns, cell nuclei, and microenvironments [ 51 , 52 , 53 , 54 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Tumor cell nuclei undergo significant changes, and when quantified, these modifications can diagnose cancer or predict the disease’s course [ 58 ]. ML-based CPATH methodologies that evaluate cellular features show promising potential in predicting cancer recurrence and survival [ 52 , 53 , 54 , 55 ]. The mutually analyzed cellular features were mostly cell nuclear area, skewness of area, and circularity.…”

Section: Resultsmentioning

confidence: 99%

“…The mutually analyzed cellular features were mostly cell nuclear area, skewness of area, and circularity. Tasoulis et al investigated the quantitative descriptors of nuclear morphometry (e.g., maximum area, skewness of area, maximum concavity) to predict recurrence, which achieved an accuracy of 92% [ 52 ]. Chen et al developed an algorithm to predict overall survival (OS) [ 53 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Khoraminia,

Fuster,

Kanwal

et al. 2023

Cancers

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Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell’s nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets—representative of clinical scenarios—are needed to address artificial intelligence’s reliability, robustness, and black box challenge.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Khoraminia,

Fuster,

Kanwal

et al. 2023

Cancers

View full text Add to dashboard Cite

show abstract

“…The simple logistic regression test result (Table 2) indicates that age, weight, BMI and all of the analyzed BIA parameters produced signi¯cant e®ects (p < 0:25) on TC level in blood, however gender and variables were not predictors to TC level in blood at ( p > 0: 25).…”

Section: Arti¯cial Neural Networkmentioning

confidence: 96%

“…Four techniques were considered in the training process; the gradient descent with momentum method (GDMBP), 22 the resilient method (RBP), 25 the scaled conjugate gradient method (SCGBP) 26 and the LevenbergÀMarquardt method (LMBP). 22 The crossvalidation process mentioned above was performed for the each of the four di®erent algorithms.…”

Section: Arti¯cial Neural Networkmentioning

confidence: 99%

Non-Invasive Approach to Predict the Cholesterol Level in Blood Using Bioimpedance and Neural Network Techniques

Mohktar

Ibrahim

Ismail

2013

Biomed. Eng. Appl. Basis Commun.

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This paper presents a new non-invasive approach to predict the status of high total cholesterol (TC) level in blood using bioimpedance and the arti¯cial neural network (ANN) techniques. The input parameters for the ANN model are acquired from a non-invasive bioelectrical impedance analysis (BIA) measurement technique. The measurement data were obtained from 260 volunteered participants. A total of 190 subject's data were used for the ANN training purpose and the remaining 70 subject's data were used for model testing. Six parameters from the BIA parameters were found to be signi¯cant predictors for TC level in blood using logistic regression analysis. The six input predictors for the ANN modeling are age, body mass index (BMI), body capacitance, basal metabolic rate, extracellular mass and lean body mass. Four ANN techniques such as the gradient descent with momentum, the resilient, the scaled conjugate gradient and the LevenbergÀMarquardt were used and compared for predicting the high TC level in the blood. The¯nding showed that the resilient method was the best model with prediction accuracy, sensitivity, speci¯city and area under the curve value obtained from the test data were 82.9%, 85.4%, 79.3% and 0.83%, respectively.

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Neural Networks and Other Machine Learning Methods in Cancer Research

Vellido

Lisboa

Computational and Ambient Intelligence

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Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence

Cited by 6 publications

References 46 publications

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Non-Invasive Approach to Predict the Cholesterol Level in Blood Using Bioimpedance and Neural Network Techniques

Neural Networks and Other Machine Learning Methods in Cancer Research

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