Research

11 pages, 1578 KiB

Open AccessArticle

Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant

by Azadeh Tabari, Brian D’Amore, Meredith Cox, Sebastian Brito, Michael S. Gee, Eric Wehrenberg-Klee, Raul N. Uppot and Dania Daye

Cancers 2023, 15(7), 2058; https://doi.org/10.3390/cancers15072058 - 30 Mar 2023

Cited by 2 | Viewed by 1573

Abstract

Background: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. Methods: Using data collected from 2005–2015, we included adult patients who (1) had a contrast-enhanced [...] Read more.

Background: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. Methods: Using data collected from 2005–2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. Results: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. Conclusions: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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12 pages, 573 KiB

Open AccessArticle

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

by Mizuho Nishio, Hidetoshi Matsuo, Yasuhisa Kurata, Osamu Sugiyama and Koji Fujimoto

Cancers 2023, 15(5), 1535; https://doi.org/10.3390/cancers15051535 - 28 Feb 2023

Cited by 5 | Viewed by 1426

Abstract

We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as [...] Read more.

We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as the development set, while those from the other institution (5456 WSIs) were used as the unseen test set. Label distribution learning (LDL) was used to address a difference in label characteristics between the development and test sets. A combination of EfficientNet (a deep learning model) and LDL was utilized to develop an automatic prediction system. Quadratic weighted kappa (QWK) and accuracy in the test set were used as the evaluation metrics. The QWK and accuracy were compared between systems with and without LDL to evaluate the usefulness of LDL in system development. The QWK and accuracy were 0.364 and 0.407 in the systems with LDL and 0.240 and 0.247 in those without LDL, respectively. Thus, LDL improved the diagnostic performance of the automatic prediction system for the grading of histopathological images for cancer. By handling the difference in label characteristics using LDL, the diagnostic performance of the automatic prediction system could be improved for prostate cancer grading. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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15 pages, 1308 KiB

Open AccessArticle

Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction

by Sebastian Starke, Alexander Zwanenburg, Karoline Leger, Klaus Zöphel, Jörg Kotzerke, Mechthild Krause, Michael Baumann, Esther G. C. Troost and Steffen Löck

Cancers 2023, 15(3), 673; https://doi.org/10.3390/cancers15030673 - 21 Jan 2023

Cited by 6 | Viewed by 2054

Abstract

Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from [...] Read more.

Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (

p = 0.044

and

p < 0.001

), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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16 pages, 2210 KiB

Open AccessArticle

CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule

by Tzu-Ning Kao, Min-Shu Hsieh, Li-Wei Chen, Chi-Fu Jeffrey Yang, Ching-Chia Chuang, Xu-Heng Chiang, Yi-Chang Chen, Yi-Hsuan Lee, Hsao-Hsun Hsu, Chung-Ming Chen, Mong-Wei Lin and Jin-Shing Chen

Cancers 2022, 14(23), 5888; https://doi.org/10.3390/cancers14235888 - 29 Nov 2022

Cited by 7 | Viewed by 1977

Abstract

It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in [...] Read more.

It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011–2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features—cluster shade, homogeneity, and run-length variance—were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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17 pages, 2899 KiB

Open AccessArticle

A Deep Learning Model System for Diagnosis and Management of Adnexal Masses

by Jianan Li, Yixin Chen, Minyu Zhang, Peifang Zhang, Kunlun He, Fengqin Yan, Jingbo Li, Hong Xu, Daniel Burkhoff, Yukun Luo, Longxia Wang and Qiuyang Li

Cancers 2022, 14(21), 5291; https://doi.org/10.3390/cancers14215291 - 27 Oct 2022

Cited by 3 | Viewed by 1305

Abstract

Appropriate clinical management of adnexal masses requires a detailed diagnosis. We retrospectively collected ultrasound images of 1559 cases from the first Center of Chinese PLA General Hospital and developed a fully automatic deep learning (DL) model system to diagnose adnexal masses. The DL [...] Read more.

Appropriate clinical management of adnexal masses requires a detailed diagnosis. We retrospectively collected ultrasound images of 1559 cases from the first Center of Chinese PLA General Hospital and developed a fully automatic deep learning (DL) model system to diagnose adnexal masses. The DL system contained five models: a detector, a mass segmentor, a papillary segmentor, a type classifier, and a pathological subtype classifier. To test the DL system, 462 cases from another two hospitals were recruited. The DL system identified benign, borderline, and malignant tumors with macro-F1 scores that varied from 0.684 to 0.791, a benefit to preventing both delayed and overextensive treatment. The macro-F1 scores of the pathological subtype classifier to categorize the benign masses varied from 0.714 to 0.831. The detailed classification can inform clinicians of the corresponding complications of each pathological subtype of benign tumors. The distinguishment between borderline and malignant tumors and inflammation from other subtypes of benign tumors need further study. The accuracy and sensitivity of the DL system were comparable to that of the expert and intermediate sonographers and exceeded that of the junior sonographer. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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13 pages, 3647 KiB

Open AccessArticle

Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge

by Byung-Hoon Kim, Hyeonhoon Lee, Kyu Sung Choi, Ju Gang Nam, Chul-Kee Park, Sung-Hye Park, Jin Wook Chung and Seung Hong Choi

Cancers 2022, 14(19), 4827; https://doi.org/10.3390/cancers14194827 - 03 Oct 2022

Cited by 8 | Viewed by 2479

Abstract

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when [...] Read more.

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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15 pages, 2803 KiB

Open AccessArticle

Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration

by Zhuohan Xin, Masashi K. Kajita, Keiko Deguchi, Shin-ichiro Suye and Satoshi Fujita

Cancers 2022, 14(19), 4587; https://doi.org/10.3390/cancers14194587 - 22 Sep 2022

Cited by 2 | Viewed by 1605

Abstract

Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate [...] Read more.

Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate an understanding of complex cell migration patterns. Here, we used electrospun fibers as the extracellular matrix for the in vitro modeling of collective cell migration, clustering of mesenchymal and epithelial cells based on trajectories, and analysis of collective migration patterns based on trajectory similarity. We normalized the trajectories to eliminate the effect of cell location on clustering and used uniform manifold approximation and projection to perform dimensionality reduction on the time-series data before clustering. When the clustering results were superimposed on the trajectories before normalization, the results still exhibited positional similarity, thereby demonstrating that this method can identify cells with similar migration patterns. The same cluster contained both mesenchymal and epithelial cells, and this result was related to cell location and cell division. These data highlight the reliability of this method in identifying consistent migration patterns during collective cell migration. This provides new insights into the epithelial–mesenchymal interactions that affect migration patterns. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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12 pages, 1031 KiB

Open AccessArticle

A Simultaneous Multiparametric ¹⁸F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer

by Valeria Romeo, Panagiotis Kapetas, Paola Clauser, Pascal A. T. Baltzer, Sazan Rasul, Peter Gibbs, Marcus Hacker, Ramona Woitek, Katja Pinker and Thomas H. Helbich

Cancers 2022, 14(16), 3944; https://doi.org/10.3390/cancers14163944 - 16 Aug 2022

Cited by 8 | Viewed by 1759

Abstract

Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to ¹⁸F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC. Methods: Eighty-six patients with 98 BC [...] Read more.

Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to ¹⁸F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC. Methods: Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous ¹⁸F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions. Results: Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images. Conclusion: A ML-based radiomics model applied to ¹⁸F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a “virtual biopsy” might be performed with radiomics signatures. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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19 pages, 10036 KiB

Open AccessArticle

Evaluation of a Deep Learning Approach to Differentiate Bowen’s Disease and Seborrheic Keratosis

by Philipp Jansen, Daniel Otero Baguer, Nicole Duschner, Jean Le’Clerc Arrastia, Maximilian Schmidt, Bettina Wiepjes, Dirk Schadendorf, Eva Hadaschik, Peter Maass, Jörg Schaller and Klaus Georg Griewank

Cancers 2022, 14(14), 3518; https://doi.org/10.3390/cancers14143518 - 20 Jul 2022

Cited by 11 | Viewed by 1725

Abstract

Background: Some of the most common cutaneous neoplasms are Bowen’s disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists’ workload, and in some cases, histological differentiation may be challenging. The potential of [...] Read more.

Background: Some of the most common cutaneous neoplasms are Bowen’s disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists’ workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen’s disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen’s diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model’s robustness. In one of the centers, the distinction between Bowen’s disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen’s disease remained challenging. Conclusions: Bowen’s disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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13 pages, 2871 KiB

Open AccessArticle

MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study

by Erika Yamazawa, Satoshi Takahashi, Masahiro Shin, Shota Tanaka, Wataru Takahashi, Takahiro Nakamoto, Yuichi Suzuki, Hirokazu Takami and Nobuhito Saito

Cancers 2022, 14(13), 3264; https://doi.org/10.3390/cancers14133264 - 03 Jul 2022

Cited by 12 | Viewed by 2989

Abstract

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated [...] Read more.

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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11 pages, 1311 KiB

Open AccessArticle

Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach

by Chorog Song, Hyunjin Park, Ho Yun Lee, Seunghak Lee, Joong Hyun Ahn and Se-Hoon Lee

Cancers 2021, 13(23), 6050; https://doi.org/10.3390/cancers13236050 - 01 Dec 2021

Cited by 3 | Viewed by 2025

Abstract

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans [...] Read more.

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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Review

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19 pages, 801 KiB

Open AccessEditor’s ChoiceReview

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

by Azadeh Tabari, Shin Mei Chan, Omar Mustafa Fathy Omar, Shams I. Iqbal, Michael S. Gee and Dania Daye

Cancers 2023, 15(1), 63; https://doi.org/10.3390/cancers15010063 - 22 Dec 2022

Cited by 11 | Viewed by 5022

Abstract

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, [...] Read more.

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in ”radiomics”, a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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26 pages, 520 KiB

Open AccessReview

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

by Barbara Lobato-Delgado, Blanca Priego-Torres and Daniel Sanchez-Morillo

Cancers 2022, 14(13), 3215; https://doi.org/10.3390/cancers14133215 - 30 Jun 2022

Cited by 11 | Viewed by 2981

Abstract

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive [...] Read more.

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

Other

Jump to: Research, Review

13 pages, 380 KiB

Open AccessSystematic Review

Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review

by Kaitlyn Alleman, Erik Knecht, Jonathan Huang, Lu Zhang, Sandi Lam and Michael DeCuypere

Cancers 2023, 15(2), 545; https://doi.org/10.3390/cancers15020545 - 16 Jan 2023

Cited by 5 | Viewed by 3147

Abstract

Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. [...] Read more.

Malignant brain tumors pose a substantial burden on morbidity and mortality. As clinical data collection improves, along with the capacity to analyze it, novel predictive clinical tools may improve prognosis prediction. Deep learning (DL) holds promise for integrating clinical data of various modalities. A systematic review of the DL-based prognostication of gliomas was performed using the Embase (Elsevier), PubMed MEDLINE (National library of Medicine), and Scopus (Elsevier) databases, in accordance with PRISMA guidelines. All included studies focused on the prognostication of gliomas, and predicted overall survival (13 studies, 81%), overall survival as well as genotype (2 studies, 12.5%), and response to immunotherapy (1 study, 6.2%). Multimodal analyses were varied, with 6 studies (37.5%) combining MRI with clinical data; 6 studies (37.5%) integrating MRI with histologic, clinical, and biomarker data; 3 studies (18.8%) combining MRI with genomic data; and 1 study (6.2%) combining histologic imaging with clinical data. Studies that compared multimodal models to unimodal-only models demonstrated improved predictive performance. The risk of bias was mixed, most commonly due to inconsistent methodological reporting. Overall, the use of multimodal data in DL assessments of gliomas leads to a more accurate overall survival prediction. However, due to data limitations and a lack of transparency in model and code reporting, the full extent of multimodal DL as a resource for brain tumor patients has not yet been realized. Full article

(This article belongs to the Special Issue Multi-Modality Imaging and Multi-Omics Approach of Cancers with Machine Learning/Deep Learning)

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