Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Gholizadeh, Neda; Simpson, John B.; Ramadan, Saadallah; Denham, J.W.; Lau, Peter; Siddique, Sabbir; Dowling, Jason; Welsh, James S.; Chalup, Stephan K.; Greer, Peter

doi:10.1002/acm2.12992

Cited by 10 publications

(12 citation statements)

References 40 publications

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“…The sharing of study protocols is crucial, since one of the main challenges of MRI is the lack of standardization. Similar protocols need to be performed with a view to the reproducibility of the data [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ].…”

Section: Perspectives and Clinical Settingsmentioning

confidence: 99%

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Granata

Muzio

Cutolo

et al. 2022

JPM

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Objective: The aim of this manuscript is to give an overview of structured reporting in radiological settings. Materials and Method: This article is a narrative review on structured reporting in radiological settings. Particularly, limitations and future perspectives are analyzed. RESULTS: The radiological report is a communication tool for the referring physician and the patients. It was conceived as a free text report (FTR) to allow radiologists to have their own individuality in the description of the radiological findings. However, this form could suffer from content, style, and presentation discrepancies, with a probability of transferring incorrect radiological data. Quality, datafication/quantification, and accessibility represent the three main goals in moving from FTRs to structured reports (SRs). In fact, the quality is related to standardization, which aims to improve communication and clarification. Moreover, a “structured” checklist, which allows all the fundamental items for a particular radiological study to be reported and permits the connection of the radiological data with clinical features, allowing a personalized medicine. With regard to accessibility, since radiological reports can be considered a source of research data, SR allows data mining to obtain new biomarkers and to help the development of new application domains, especially in the field of radiomics. Conclusions: Structured reporting could eliminate radiologist individuality, allowing a standardized approach.

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Section: Perspectives and Clinical Settingsmentioning

confidence: 99%

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Granata

Muzio

Cutolo

et al. 2022

JPM

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“…Literature research revealed 57 original papers computing RF on multiparametric magnetic resonance tomography (mpMRI) imaging (see Table 1). The most common segmented VOI was intraprostatic tumor (n = 48) , of which five studies focused on tumor location in transitional zone (TZ) [72,73,81,95,112] and five studies in peripheral zone (PZ) [68,71,76,82,95]. 14 studies selected the prostate as VOI [69,71,73,78,82,87,96,97,104,110,112,[115][116][117][118] of which one study analyzed the prostate excluding urethra and intraprostatic dominant lesions [97] and one study differentiated between different prostate zones [115].…”

Section: Mrimentioning

confidence: 99%

“…RFs were extracted from T2-weighted images (T2w, n = 52) [66][67][68][69][70][71][72][73][74][75][76][78][79][80][81][82][83][84][85][86][87][88][89][90][91][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][109][110][111][112][113][116][117][118][119][120][121][122], appare...…”

Section: Mrimentioning

confidence: 99%

“…PCa detection (n = 19) [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82]114,115], Gleason Score (GS) discrimination or upgrading from biopsy to prostatectomy (n = 22) [83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100]116,[119][120][121], extracapsular extension (n = 5) [90,[103][104][105]120], biochemical recurrence (n=4) [108][109][110]117], segmentation (n = 3) …”

Section: Mrimentioning

confidence: 99%

“…[68][69][70]75]. Five studies considered the tumor location (TZ vs. PZ) for RF based PCa detection [71][72][73]76,81]. Two of these demonstrated improved PZ and TZ lesion classification with ADC based RF [71] and a high ROC-AUC value of 0.86 [72].…”

Section: Mrimentioning

confidence: 99%

See 2 more Smart Citations

Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

Spohn¹,

Bettermann²,

Bamberg³

et al. 2021

Theranostics

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Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics -a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.

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MRI‐based radiomic features for identifying recurrent prostate cancer after proton radiation therapy

Gumus,

Contreras,

Al‐Toubat

et al. 2024

J Applied Clin Med Phys

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PurposeMagnetic Resonance Imaging (MRI) evaluation of recurrent prostate cancer (PCa) following proton beam therapy is challenging due to radiation‐induced tissue changes. This study aimed to evaluate MRI‐based radiomic features so as to identify the recurrent PCa after proton therapy.MethodsWe retrospectively studied 12 patients with biochemical recurrence (BCR) following proton therapy. Two experienced radiologists identified prostate lesions from multi‐parametric MRI (mpMRI) images post‐proton therapy and marked control regions of interest (ROIs) on the contralateral side of the prostate gland. A total of 210 radiomic features were extracted from lesions and control regions on the T2‐weighted (T2WI) and Apparent Diffusion Coefficient (ADC) image series. Recursive Feature Elimination with Cross‐Validation method (RFE‐CV) was used for feature selection. A Multilayer Perceptron (MLP) neural network was developed to classify three classes: cancerous, benign, and healthy tissue. The 12‐core biopsy results were used as the gold standard for the segmentations. The classifier performance was measured using specificity, sensitivity, the area under receiver operating characteristic curve (AUC), and other statistical indicators.ResultsBased on biopsy results, 10 lesions were identified as PCa recurrence while eight lesions were confirmed to be benign. Ten radiomic features (10/210) were selected to build the multi‐class classifier. The radiomics classifier gave an accuracy of 0.83 in identifying cancerous, benign, and healthy tissue with a sensitivity of 0.80 and specificity of 0.85. The model yielded an AUC of 0.87, 95% CI [0.72–1.00] in differentiating cancer from the benign and healthy tissues.ConclusionsOur proof‐of‐concept study demonstrates the potential of using radiomic features as part of the differential diagnosis of PCa on mpMRI following proton therapy. The results need to be validated in a larger cohort.

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Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Cited by 10 publications

References 40 publications

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

MRI‐based radiomic features for identifying recurrent prostate cancer after proton radiation therapy

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