Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images. METHODS: Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status. Performance was evaluated by the ROC Curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: The network achieved a mean AUC of 0.80 ± 0.06. The best-preforming fold had a sensitivity of 0.86 and specificity of 0.92 at the Youden's index. The PPV, NPV, and F1 scores are 0.97, 0.71, and 0.82, respectively. CONCLUSIONS: A fully automated CNN can characterize the HPV status of OPSCC patients with high sensitivity and specificity. Further refinement of this algorithm has the potential to provide a non-invasive tool to guide clinical management.

Predictive value of the hemispheric magnetic resonance angiography score on the development of indirect pial synangiosis after combined revascularization surgery for adult moyamoya disease.

PURPOSE: It is difficult to precisely predict indirect bypass development in the context of combined bypass procedures in moyamoya disease (MMD). We aimed to investigate the predictive value of magnetic resonance angiography (MRA) signal intensity in the peripheral portion of the major cerebral arteries for indirect bypass development in adult patients with MMD. METHODS: We studied 93 hemispheres from 62 adult patients who underwent combined direct and indirect revascularization between 2005 and 2019 and genetic analysis for RNF213 p.R4810K. The signal intensity of the peripheral portion of the major intracranial arteries during preoperative MRA was graded as a hemispheric MRA score (0-3 in the middle cerebral artery and 0-2 in the anterior cerebral and posterior cerebral arteries, with a high score representing low visibility) according to each vessel's visibility. Postoperative bypass development was qualitatively evaluated using MRA, and we evaluated the correlation between preoperative factors, including the hemispheric MRA score and bypass development, using univariate and multivariate analyses. RESULTS: A good indirect bypass was observed in 70% of the hemispheres. Hemispheric MRA scores were significantly higher in hemispheres with good indirect bypass development than in those with poor indirect bypass development (median: 3 vs. 1; p < 0.0001). Multiple logistic regression analysis revealed hemispheric MRA score as an independent predictor of good indirect bypass development (odds ratio, 2.1; 95% confidence interval, 1.3-3.6; p < 0.01). The low hemispheric MRA score (< 2) and wild-type RNF213 predicted poor indirect bypass development with a specificity of 0.92. CONCLUSION: Hemispheric MRA score was a predictive factor for indirect bypass development in adult patients who underwent a combined bypass procedure for MMD. Predicting poor indirect bypass development may lead to future tailored bypass surgeries for MMD.

Increased CD44 expression in primary meningioma: its clinical significance and association with peritumoral brain edema.

OBJECTIVE: CD44 is a major cell surface receptor involved in cell adhesion and migration. The overexpression of CD44 is a poor prognostic factor in many neoplasms, including meningiomas. The aim of this study was to investigate the association between CD44 gene expression and clinical signatures of primary meningiomas. METHODS: CD44 gene expression was quantitatively evaluated by snap freezing tumor tissues obtained from 106 patients with primary meningioma. The relationships between CD44 expression and clinical signatures of meningiomas, including histological malignancy, tumor volume, and peritumoral brain edema (PTBE), were analyzed. PTBE was assessed using the Steinhoff classification (SC) system (from SC 0 to SC III). RESULTS: CD44 gene expression in WHO grade 2 and 3 meningiomas was significantly higher than that in grade 1 meningiomas. In addition, CD44 expression increased with the severity of PTBE. Particularly, among the grade 1 meningiomas or small-sized tumors (maximum tumor diameter < 43 mm), CD44 expression in tumors with severe PTBE (SC II or III) was significantly higher than that in tumors without or with mild PTBE (SC 0 or I). Multivariate logistic regression analysis also revealed that overexpression of CD44 was an independent significant factor of severe PTBE development in primary meningiomas. CONCLUSIONS: In addition to tumor cell aggressiveness, CD44 expression promotes the development of PTBE in meningioma. Since PTBE is a strong factor of tumor-related epilepsy or cognitive dysfunction in patients with meningioma, CD44 is thus a potential therapeutic target in meningioma with PTBE.

Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach.

PURPOSE: To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance. MATERIALS AND METHODS: We divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification. RESULTS: The CNN model's diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses. CONCLUSIONS: This CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Comparing accuracy of machine learning approaches to identifying parathyroid adenomas: Lessons and new directions.

PURPOSE: The purpose of this investigation is to understand the accuracy of machine learning techniques to detect biopsy-proven adenomas from similar appearing lymph nodes and factors that influence accuracy by comparing support vector machine (SVM) and bidirectional Long short-term memory (Bi-LSTM) analyses. This will provide greater insight into how these tools could integrate multidimensional data and aid the detection of parathyroid adenomas consistently and accurately. METHODS: Ninety-nine patients were identified; 93 4D-CTs of patients with pathology-proven parathyroid adenomas were reviewed; 94 parathyroid adenomas and 112 lymph nodes were analyzed. A 2D slice through the lesions in each phase was used to perform sequence classification with ResNet50 as the pre-trained network to construct the Bi-LSTM model, and the mean enhancement curves were used to form an SVM model. The model characteristics and accuracy were calculated for the training and validation data sets. RESULTS: On the training data, the area under the curve (AUC) of the Bi-LSTM was 0.99, while the SVM was 0.95 and statistically significant on the DeLong test. The overall accuracy of the Bi-LSTM on the validation data set was 92 %, while the SVM was 88 %. The accuracy for parathyroid adenomas specifically was 93 % for the Bi-LSTM and 83 % for the SVM model. CONCLUSION: Enhancement characteristics are a distinguishing feature that accurately identifies parathyroid adenomas alone. The Bi-LSTM performs statistically better in identifying parathyroid adenomas than the SVM analysis when using both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes. SUMMARY STATEMENT: The Bi-LSTM more accurately identifies parathyroid adenomas than the SVM analysis, which uses both morphologic and enhancement information to distinguish between parathyroid adenomas and lymph nodes, performs statistically better.

Multiparametric machine learning algorithm for human papillomavirus status and survival prediction in oropharyngeal cancer patients.

BACKGROUND: Human papillomavirus (HPV) status influences prognosis in oropharyngeal cancer (OPC). Identifying high-risk patients are critical to improving treatment. We aim to provide a noninvasive opportunity for managing OPC patients by training multiple machine learning pipelines to determine the best model for characterizing HPV status and survival. METHODS: Multi-parametric algorithms were designed using a 492 OPC patient database. HPV status incorporated age, sex, smoking/drinking habits, cancer subsite, TNM, and AJCC 7th edition staging. Survival considered HPV model inputs plus HPV status. Patients were split 4:1 training: testing. Algorithm efficacy was assessed through accuracy and area under the receiver operator characteristic curve (AUC). RESULTS: From 31 HPV status models, ensemble yielded 0.83 AUC and 78.7% accuracy. From 38 survival models, ensemble yielded 0.91 AUC and 87.7% accuracy. CONCLUSION: Results reinforce artificial intelligence's potential to use tumor imaging and patient characterizations for HPV status and outcome prediction. Utilizing these algorithms can optimize clinical guidance and patient care noninvasively.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI.

We investigated the ability of echo-planar imaging with L1-regularized iterative sensitivity encoding-based diffusion-weighted imaging (DWI) to improve the image quality and reduce the scanning time in prostate magnetic resonance imaging. We retrospectively analyzed 109 cases of prostate magnetic resonance imaging. We compared variables in the quantitative and qualitative assessments among 3 imaging groups: conventional parallel imaging-based DWI (PI-DWI) with an acquisition time of 3 minutes 15 seconds; echo-planar imaging with L1-regularized iterative sensitivity encoding-based DWI (L1-DWI) with a normal acquisition time (L1-DWINEX12) of 3 minutes 15 seconds; and L1-DWI with a half acquisition time (L1-DWINEX6) of 1 minute 45 seconds. As a quantitative assessment, the signal-to-noise ratio (SNR) of DWI (SNR-DWI), the contrast-to-noise ratio (CNR) of DWI (CNR-DWI), and the CNR of apparent diffusion coefficient were measured. As a qualitative assessment, the image quality and visual detectability of prostate carcinoma were evaluated. In the quantitative analysis, L1-DWINEX12 showed significantly higher SNR-DWI than PI-DWI (P = .0058) and L1-DWINEX6 (P < .0001). In the qualitative analysis, the image quality score for L1-DWINEX12 was significantly higher than those of PI-DWI and L1-DWINEX6. A non-inferiority assessment demonstrated that L1-DWINEX6 was non-inferior to PI-DWI in terms of both quantitative CNR-DWI and qualitative grading of image quality with a <20% inferior margin. L1-DWI successfully demonstrated a reduced scanning time while maintaining good image quality.

Chronological Volume Changes of the Temporal Muscle Pedicle Used for Encephalo-myo-synangiosis in Combined Revascularization for Moyamoya Disease: A Prospective Observational Study.

Although postoperative neurological events due to brain compression by the swollen temporal muscle are a rare complication, the chronological volume changes of the temporal muscle pedicle and their clinical impact have not yet been documented. This prospective observational study aimed to investigate the chronological volume changes in the temporal muscle pedicle in Moyamoya disease (MMD). Eighteen consecutive combined revascularization procedures using the temporal muscle were performed for symptomatic MMD in 2021. The postoperative pedicle volume was quantified using repeated computed tomography images on postoperative days (PODs) 0, 1, 7, 14, and 30. Postoperative neurological events with radiological evaluations and collateral development evaluated using magnetic resonance angiography obtained 6 months after surgery were studied. On average, the postoperative temporal muscle pedicle volume was most significantly increased by as much as 112% ± 9.6% on POD 7 (P < 0.001) and decreased by as little as 52% ± 21% on POD 30 (P < 0.0001) relative to POD 0. One exceptional patient (overall incidence, 5.6%) demonstrated postoperative transient neurological events due to brain compression by the swollen temporal muscle with decreased focal cerebral blood flow in the adjacent cortical area. The postoperative collateral development via direct and indirect revascularizations was confirmed in 16 (89%) and 12 (67%) hemispheres, respectively. All patients, except for one rebleeding case, showed independent outcomes at the mean latest follow-up period on 290 ± 96 days after surgery. Our observations confirmed the temporal profile of muscle pedicle volume changes after combined revascularization. Through routine attempts to avoid the unfavorable effects of temporal muscle swelling, combined revascularization can provide favorable outcomes in symptomatic MMD.

Non-Gaussian model-based diffusion-weighted imaging of oral squamous cell carcinoma: associations with Ki-67 proliferation status.

OBJECTIVES: To investigate possible associations between diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian model fitting and Ki-67 status in patients with oral squamous cell carcinoma (OSCC). METHODS: Twenty-four patients with newly diagnosed OSCC were prospectively recruited. DWI was performed using six b-values (0-2500). The diffusion-related parameters of kurtosis value (K), kurtosis-corrected diffusion coefficient (DK), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), slow diffusion coefficient (Dslow), and apparent diffusion coefficient (ADC) were calculated from four diffusion fitting models. Ki-67 status was categorized as low (Ki-67 percentage score < 20%), middle (20-50%), or high (> 50%). Kruskal-Wallis tests were performed between each non-Gaussian diffusion model parameters and Ki-67 grade. RESULTS: The Kruskal-Wallis tests revealed that multiple parameters (K, ADC, Dk, DDC and Dslow) showed statistically significant differences between the three levels of Ki-67 status (K: p = 0.020, ADC: p = 0.012, Dk: p = 0.027, DDC: p = 0.007 and Dslow: p = 0.026). CONCLUSIONS: Several non-Gaussian diffusion model parameters and ADC values were significantly associated with Ki-67 status and have potential as promising prognostic biomarkers in patients with OSCC.

MR vessel-encoded arterial spin labeling with the placement of metallic items to visualize the territorial blood flow after extracranial-intracranial bypass surgery: a proof-of-concept study.

BACKGROUND: Depiction of bypass blood flow in patients who received extracranial-intracranial (EC-IC) bypass surgery is important for patient care. PURPOSE: To develop a vessel-encoded arterial spin labeling (VE-ASL) method using surgical staples as a magnetic resonance (MR)-conditional product in patients who received EC-IC bypass surgery. MATERIAL AND METHODS: Pseudo-continuous labeling was used for VE-ASL acquisition with a 3-T MR unit. First, an experimental study was conducted to determine the appropriate number of surgical staples to obtain a spatially sufficient saturation effect. Thereafter, four healthy normal volunteers underwent a VE-ASL study to confirm the sufficiency of the saturation effect to the right or left common carotid artery. Finally, VE-ASL scanning was performed in seven patients after EC-IC bypass surgery to confirm the ability of VE-ASL to visualize the territorial bypass perfusion. All qualitative evaluation was performed by two neuroradiologists using a 3-point grading system (2 = good, 1 = moderate, 0 = poor). RESULTS: A quantity of 200 staples was found to be appropriate for VE-ASL scanning. In healthy volunteers, one neuroradiologist rated the images of all four cases as good, while the other rated three cases as good and one case as moderate. For the seven patients after EC-IC bypass surgery, one neuroradiologist rated all seven cases as good, and the other rated six cases as good and one case as moderate. CONCLUSION: VE-ASL using surgical staples might be useful for the evaluation of territorial bypass perfusion in patients after EC-IC bypass surgery.

Long-term consequences of residual lesions after chemoradiotherapy in patients with germinoma at onset.

OBJECTIVE: In patients with intracranial germ cell tumors, residual lesions are sometimes observed after completion of primary chemoradiotherapy. Although salvage resection of these end-of-treatment residual lesions is recommended for patients with nongerminomatous germ cell tumors, the necessity of early salvage resection for those with germinoma is not clear. The aim of this study was to investigate the frequency of residual germinoma lesions after primary chemoradiotherapy, as well as their management, long-term consequences, and prognosis. METHODS: The authors retrospectively reviewed patients who were primarily treated for germinoma between 2002 and 2021. Residual lesions were evaluated with MRI with and without contrast enhancement within 2 weeks after chemoradiotherapy. The decision to perform salvage resection of residual lesions was at the discretion of the treating physicians. The change in appearance of residual lesions was assessed with serial MRI. Overall survival (OS), progression-free survival (PFS), and recurrence pattern were also investigated. RESULTS: Sixty-nine patients were treated with chemoradiotherapy for germinoma, with a mean follow-up period of 108 months. Residual lesions were radiologically observed in 30 patients (43.5%). Among these, 5 patients (3 with pineal lesions and 2 with basal ganglia lesions) underwent salvage resection. Pathological examination revealed teratomatous components in 3 patients, whereas no tumoral components were identified in 2 patients. One patient with a basal ganglia lesion showed worsening of hemiparesis postoperatively. The remaining 25 patients received watchful observation without surgical intervention. Chronological periodic radiological change in residual lesions was evaluated in 21 patients. One year after primary treatment, the size of the residual lesions was stable and had decreased in 10 and 11 patients, respectively. None of the lesions increased in size. The 10-year PFS and OS rates were 96.7% and 97.3% in patients without residual lesions (n = 39), and 87.1% and 100% in patients with residual lesions (n = 30), respectively. Presence of residual lesions had no significant effect on PFS or OS. All recurrences occurred at distant sites or via dissemination without progression of the primary tumor site, regardless of the presence of residual lesion. CONCLUSIONS: End-of-treatment residual lesions are not rare in patients with germinoma, and these residual lesions seldom show progression. Because of the potential risk of surgical complications, the indication for early salvage surgery for residual lesions should be carefully determined. Watchful observation is recommended for the majority of these cases.

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor.

BACKGROUND: In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images. METHODS: A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model. RESULTS: The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively). CONCLUSION: The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.

The utility of diffusion-weighted T2 mapping for the prediction of histological tumor grade in patients with head and neck squamous cell carcinoma.

Background: In head and neck cancers, histopathological information is important for the determination of the tumor characteristics and for predicting the prognosis. The aim of this study was to assess the utility of diffusion-weighted T2 (DW-T2) mapping for the evaluation of tumor histological grade in patients with head and neck squamous cell carcinoma (SCC). Methods: The cases of 41 patients with head and neck SCC (21 well/moderately and 17 poorly differentiated SCC) were retrospectively analyzed. All patients received MR scanning using a 3-Tesla MR unit. The conventional T2 value, DW-T2 value, ratio of DW-T2 value to conventional T2 value, and apparent diffusion coefficient (ADC) were calculated using signal information from the DW-T2 mapping sequence with a manually placed region of interest (ROI). Results: ADC values in the poorly differentiated SCC group were significantly lower than those in the moderately/well differentiated SCC group (P<0.05). The ratio of DW-T2 value to conventional T2 value was also significantly different between poorly and moderately/well differentiated SCC groups (P<0.01). Receiver operating characteristic (ROC) curve analysis of ADC values showed a sensitivity of 0.76, specificity of 0.67, positive predictive value (PPV) of 0.62, negative predictive value (NPV) of 0.8, accuracy of 0.71 and area under the curve (AUC) of 0.73, whereas the ROC curve analysis of the ratio of DW-T2 value to conventional T2 value showed a sensitivity of 0.76, specificity of 0.83, PPV of 0.76, NPV of 0.83, accuracy of 0.8 and AUC of 0.82. Conclusions: DW-T2 mapping might be useful as supportive information for the determination of tumor histological grade in patients with head and neck SCC.

Amide proton transfer imaging for the determination of human papillomavirus status in patients with oropharyngeal squamous cell carcinoma.

The aim of this study was to investigate the utility of amide proton transfer (APT) imaging for the determination of human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (SCC). Thirty-one patients with oropharyngeal SCC were retrospectively evaluated. All patients underwent amide proton transfer imaging using a 3T magnetic resonance (MR) unit. Patients were divided into HPV-positive and -negative groups depending on the pathological findings in their primary tumor. In APT imaging, the primary tumor was delineated with a polygonal region of interest (ROI). Signal information in the ROI was used to calculate the mean, standard deviation (SD) and coefficient of variant (CV) of the APT signals (APT mean, APT SD, and APT CV, respectively). The value of APT CV in the HPV-positive group (0.43 ±â€…0.04) was significantly lower than that in the HPV-negative group (0.48 ±â€…0.04) (P = .01). There was no significant difference in APT mean (P = .82) or APT SD (P = .13) between the HPV-positive and -negative groups. Receiver operating characteristic (ROC) curve analysis of APT CV had a sensitivity of 0.75, specificity of 0.8, positive predictive value of 0.75, negative predictive value of 0.8, accuracy of 0.77 and area under the curve (AUC) of 0.8. The APT signal in the HPV-negative group was considered heterogeneous compared to the HPV-positive group. This information might be useful for the determination of HPV status in patients with oropharyngeal SCC.

Artificial intelligence for nuclear medicine in oncology.

As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.

Multiparametric Analysis of Tumor Morphological and Functional MR Parameters Potentially Predicts Local Failure in Pharynx Squamous Cell Carcinoma Patients.

Purpose : To predict local control / failure by a multiparametric approach using magnetic resonance (MR)-derived tumor morphological and functional parameters in pharynx squamous cell carcinoma (SCC) patients. Materials and Methods : Twenty-eight patients with oropharyngeal and hypopharyngeal SCCs were included in this study. Quantitative morphological parameters and intratumoral characteristics on T2-weighted images, tumor blood flow from pseudo-continuous arterial spin labeling, and tumor diffusion parameters of three diffusion models from multi-b-value diffusion-weighted imaging as well as patients' characteristics were analyzed. The patients were divided into local control / failure groups. Univariate and multiparametric analysis were performed for the patient group division. Results : The value of morphological parameter of 'sphericity' and intratumoral characteristic of 'homogeneity' was revealed respectively significant for the prediction of the local control status in univariate analysis. Higher diagnostic performance was obtained with the sensitivity of 0.8, specificity of 0.75, positive predictive value of 0.89, negative predictive value of 0.6 and accuracy of 0.79 by multiparametric diagnostic model compared to results in the univariate analysis. Conclusion : A multiparametric analysis with MR-derived quantitative parameters may be useful to predict local control in pharynx SCC patients. J. Med. Invest. 68 : 354-361, August, 2021.

Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.

BACKGROUND: This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. METHODS: One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient's clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. RESULTS: Training sessions were successfully performed with an accuracy of 74-89%. ROC curve analyses revealed an AUC of 0.61-0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient's local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model. CONCLUSIONS: Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs.