Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks

Artificial Intelligence Imaging Diagnosis Using Super-Resolution and Three-Dimensional Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center

BACKGROUND

Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, the accuracy of present diagnostic modalities has room for improvement.

OBJECTIVE

The study aimed to establish a high-precision diagnostic method for lymph node metastasis of low rectal cancer using artificial intelligence.

DESIGN

A retrospective observational study.

SETTINGS

A single cancer center and a college of engineering in Japan.

PATIENTS

Patients with low rectal adenocarcinoma who underwent proctectomy, bilateral lateral pelvic lymph node dissection, and contrast-enhanced multidetector row CT (slice ≤1 mm) between July 2015 and August 2021 were included in the present study. All pelvic lymph nodes from the aortic bifurcation to the upper edge of the anal canal were extracted, regardless of whether within or beyond the total mesenteric excision area, and pathological diagnoses were annotated for training and validation.

MAIN OUTCOME MEASURES

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

RESULTS

A total of 596 pathologically negative nodes and 43 positive nodes from 52 patients were extracted and annotated. Four diagnostic methods, with and without using super-resolution images and with and without using 3-dimensional shape data, were performed and compared. The super-resolution + 3-dimensional shape data method had the best diagnostic ability for the combination of sensitivity, negative predictive value, and accuracy (0.964, 0.966, and 0.968, respectively), whereas the super-resolution only method had the best diagnostic ability for the combination of specificity and positive predictive value (0.994 and 0.993, respectively).

LIMITATIONS

Small number of patients at a single center and the lack of external validation.

CONCLUSIONS

Our results enlightened the potential of artificial intelligence for the method to become another game changer in the diagnosis and treatment of low rectal cancer. See Video Abstract .

DIAGNSTICO POR IMGENES CON INTELIGENCIA ARTIFICIAL MEDIANTE SUPERRESOLUCIN Y FORMA D PARA LA METSTASIS EN LOS GANGLIOS LINFTICOS DEL CNCER DE RECTO BAJO UN ESTUDIO PILOTO DE UN SOLO CENTRO

ANTECEDENTES:Aunque el diagnóstico preoperatorio preciso de metástasis en los ganglios linfáticos es esencial para optimizar las estrategias de tratamiento para el cáncer de recto bajo, la precisión de las modalidades de diagnóstico actuales tiene margen de mejora.OBJETIVO:Establecer un método de diagnóstico de alta precisión para las metástasis en los ganglios linfáticos del cáncer de recto bajo utilizando inteligencia artificial.DISEÑO:Un estudio observacional retrospectivo.AJUSTE:Un único centro oncológico y una facultad de ingeniería en Japón.PACIENTES:En el presente estudio se incluyeron pacientes con adenocarcinoma rectal bajo sometidos a proctectomía, disección bilateral de ganglios linfáticos pélvicos laterales y tomografía computarizada con múltiples detectores con contraste (corte ≤1 mm) entre julio de 2015 y agosto de 2021. Se resecaron todos los ganglios linfáticos pélvicos desde la bifurcación aórtica hasta el borde superior del canal anal, independientemente de si estaban dentro o más allá del área de escisión mesentérica total, y se registraron los diagnósticos patológicos para entrenamiento y validación.PRINCIPALES MEDIDAS DE RESULTADO:Sensibilidad, especificidad, valor predictivo positivo, valor predictivo negativo y precisión.RESULTADOS:Se extrajeron y registraron un total de 596 ganglios patológicamente negativos y 43 positivos de 52 pacientes. Se realizaron y compararon cuatro métodos de diagnóstico, con y sin imágenes de súper resolución y sin datos de imagen en 3D. El método de superresolución + datos de imagen en 3D tuvo la mejor capacidad de diagnóstico para la combinación de sensibilidad, valor predictivo negativo y precisión (0,964, 0,966 y 0,968, respectivamente), mientras que el método de súper resolución solo tuvo la mejor capacidad de diagnóstico para la combinación de especificidad y valor predictivo positivo (0,994 y 0,993, respectivamente).LIMITACIONES:Pequeño número de pacientes en un solo centro y falta de validación externa.CONCLUSIONES:Nuestros resultados iluminan el potencial de la inteligencia artificial para que el método se convierta en otro elemento de cambio en el diagnóstico y tratamiento del cáncer de recto bajo. (Traducción ---Dr. Fidel Ruiz Healy ).

Segmentation of mediastinal lymph nodes in CT with anatomical priors

PURPOSE

Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs.

METHODS

We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D off-the-shelf nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance.

RESULTS

For LNs with short axis diameter ≥ 8 mm, the 3D cascade nnUNet model obtained the highest Dice score of 67.9 ± 23.4 and lowest Hausdorff distance error of 22.8 ± 20.2. For LNs of all sizes, the Dice score was 58.7 ± 21.3 and this represented a ≥ 10% improvement over a recently published approach evaluated on the same test dataset.

CONCLUSION

To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has the potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Detection of abdominopelvic lymph nodes in multi-parametric MRI

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

Construction and validation of machine learning models for predicting distant metastases in newly diagnosed colorectal cancer patients: A large-scale and real-world cohort study

BACKGROUND

More accurate prediction of distant metastases (DM) in patients with colorectal cancer (CRC) would optimize individualized treatment and follow-up strategies. Multiple prediction models based on machine learning have been developed to assess the likelihood of developing DM.

METHODS

Clinicopathological features of patients with CRC were obtained from the National Cancer Center (NCC, China) and the Surveillance, Epidemiology, and End Results (SEER) database. The algorithms used to create the prediction models included random forest (RF), logistic regression, extreme gradient boosting, deep neural networks, and the K-Nearest Neighbor machine. The prediction models' performances were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

In total, 200,958 patients, 3241 from NCC and 197,717 CRC from SEER were identified, of whom 21,736 (10.8%) developed DM. The machine-learning-based prediction models for DM were constructed with 12 features remaining after iterative filtering. The RF model performed the best, with areas under the ROC curve of 0.843, 0.793, and 0.806, respectively, on the training, test, and external validation sets. For the risk stratification analysis, the patients were separated into high-, middle-, and low-risk groups according to their risk scores. Patients in the high-risk group had the highest incidence of DM and the worst prognosis. Surgery, chemotherapy, and radiotherapy could significantly improve the prognosis of the high-risk and middle-risk groups, whereas the low-risk group only benefited from surgery and chemotherapy.

CONCLUSION

The RF-based model accurately predicted the likelihood of DM and identified patients with CRC in the high-risk group, providing guidance for personalized clinical decision-making.

Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (n = 589) and internal test cohort (n = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, P < .001; C index = 0.689, P < .001) and performing comparably with senior radiologists (AUC = 0.79, P = .21; C index = 0.788, P = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, P < .001; C index = 0.798, P < .001) and senior radiologists (AUC = 0.88, P < .001; C index = 0.869, P < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. Keywords: MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. Published under a CC BY 4.0 license.

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review

PURPOSE

The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat-ment strategies for patients with rectal cancer.

METHODS

This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.

RESULTS

AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer-ing noninvasive and cost-effective alternatives for identifying genetic mutations.

CONCLUSION

Image-based AI studies for rectal can-cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Universal detection and segmentation of lymph nodes in multi-parametric MRI

PURPOSE

Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance.

METHODS

We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study. The T2FS and DWI series in 38 studies (38 patients) were co-registered and blended together using a selective data augmentation technique, such that traits of both series were visible in the same volume. A mask RCNN model was subsequently trained for universal detection and segmentation of 3D LNs.

RESULTS

Experiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of [Formula: see text]%, sensitivity of [Formula: see text]% at 4 false positives (FP) per volume, and dice score of [Formula: see text]%. This represented an improvement of [Formula: see text]% in precision, [Formula: see text]% in sensitivity at 4 FP/volume, and [Formula: see text]% in dice score, respectively, over current approaches evaluated on the same dataset.

CONCLUSION

Our pipeline universally detected and segmented both metastatic and non-metastatic nodes in mpMRI studies. At test time, the input data used by the trained model could either be the T2FS series alone or a blend of co-registered T2FS and DWI series. Contrary to prior work, this eliminated the reliance on both the T2FS and DWI series in a mpMRI study.

3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net

Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best.

Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study

PURPOSE

There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC).

METHODS

This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively.

RESULTS

In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919-1.000) and 0.898 (95% CI 0.824-0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation.

CONCLUSION

The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.

A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation

PURPOSE

This study aimed to build a deep learning system using enhanced computed tomography (CT) portal-phase images for predicting colorectal cancer patients' preoperative staging and RAS gene mutation status.

METHODS

The contrast-enhanced CT image dataset comprises the CT portal-phase images from a retrospective cohort of 231 colorectal cancer patients. The deep learning system was developed via migration learning for colorectal cancer detection, staging, and RAS gene mutation status prediction. This study used pre-trained Yolov7, vision transformer (VIT), swin transformer (SWT), EfficientNetV2, and ConvNeXt. 4620, and contrast-enhanced CT images and annotated tumor bounding boxes were included in the tumor identification and staging dataset. A total of 19,700 contrast-enhanced CT images comprise the RAS gene mutation status prediction dataset.

RESULTS

In the validation cohort, the Yolov7-based detection model detected and staged tumors with a mean accuracy precision (IoU = 0.5) (mAP_0.5) of 0.98. The area under the receiver operating characteristic curve (AUC) in the test set and validation set for the VIT-based prediction model in predicting the mutation status of the RAS genes was 0.9591 and 0.9554, respectively. The detection network and prediction network of the deep learning system demonstrated great performance in explaining contrast-enhanced CT images.

CONCLUSION

In this study, a deep learning system was created based on the foundation of contrast-enhanced CT portal-phase imaging to preoperatively predict the stage and RAS mutation status of colorectal cancer patients. This system will help clinicians choose the best treatment option to increase colorectal cancer patients' chances of survival and quality of life.

Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends

This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.

FasterRib: A deep learning algorithm to automate identification and characterization of rib fractures on chest computed tomography scans

OBJECTIVE

Characterizing and enumerating rib fractures are critical to informing clinical decisions, yet in-depth characterization is rarely performed because of the manual burden of annotating these injuries on computed tomography (CT) scans. We hypothesized that our deep learning model, FasterRib , could predict the location and percentage displacement of rib fractures using chest CT scans.

METHODS

The development and internal validation cohort comprised more than 4,700 annotated rib fractures from 500 chest CT scans within the public RibFrac. We trained a convolutional neural network to predict bounding boxes around each fracture per CT slice. Adapting an existing rib segmentation model, FasterRib outputs the three-dimensional locations of each fracture (rib number and laterality). A deterministic formula analyzed cortical contact between bone segments to compute percentage displacements. We externally validated our model on our institution's data set.

RESULTS

FasterRib predicted precise rib fracture locations with 0.95 sensitivity, 0.90 precision, 0.92 f1 score, with an average of 1.3 false-positive fractures per scan. On external validation, FasterRib achieved 0.97 sensitivity, 0.96 precision, and 0.97 f1 score, and 2.24 false-positive fractures per scan. Our publicly available algorithm automatically outputs the location and percent displacement of each predicted rib fracture for multiple input CT scans.

CONCLUSION

We built a deep learning algorithm that automates rib fracture detection and characterization using chest CT scans. FasterRib achieved the highest recall and the second highest precision among known algorithms in literature. Our open source code could facilitate FasterRib's adaptation for similar computer vision tasks and further improvements via large-scale external validation.

LEVEL OF EVIDENCE

Diagnostic Tests/Criteria; Level III.

Progress in the diagnosis of lymph node metastasis in rectal cancer: a review

Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.

Microfluidic Technology, Artificial Intelligence, and Biosensors As Advanced Technologies in Cancer Screening: A Review Article

Cancer screening techniques aim to detect premalignant lesions and enable early intervention to delay the onset of cancer while keeping incidence constant. Technology advancements have led to the development of powerful tools such as microfluidic technology, artificial intelligence, machine learning algorithms, and electrochemical biosensors to aid in early cancer detection. Non-invasive cancer screening methods like virtual colonoscopy and endoscopic ultrasonography have also been developed to provide comprehensive pictures of organs and detect cancer early. This review article provides an overview of recent advances in cancer screening in microfluidic technology, artificial intelligence, and biomarkers through a narrative literature search. Microfluidic devices enable easy handling of sub-microliter volumes and have become a promising tool for cancer detection, drug screening, and modeling angiogenesis and metastasis in cancer research. Machine learning and artificial intelligence have shown high accuracy in oncology-related diagnostic imaging, reducing the manual steps in lesion detection and providing standardized and accurate results, with potential for global standardization in areas like colon polyps, breast cancer, and primary and metastatic brain cancer. A biomarker-based cancer diagnosis is promising for early detection and effective therapy, and electrochemical biosensors integrated with nanoparticles offer multiplexing and amplification capabilities. Understanding these advanced technologies' basics, achievements, and challenges is crucial for advancing their use in oncology.

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review

OBJECTIVES

There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.

DESIGN

Systematic review.

DATA SOURCES

Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied.

ELIGIBILITY CRITERIA

Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)).

RESULTS

Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5-2440) and 37 (range: 10-1045) patients, respectively. Diagnostic performance of models varied (range: 70%-95% sensitivity, 53%-98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability.

CONCLUSIONS

AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority.

PROSPERO REGISTRATION NUMBER

CRD42021237249.

Intelligent oncology: The convergence of artificial intelligence and oncology

With increasingly explored ideologies and technologies for potential applications of artificial intelligence (AI) in oncology, we here describe a holistic and structured concept termed intelligent oncology. Intelligent oncology is defined as a cross-disciplinary specialty which integrates oncology, radiology, pathology, molecular biology, multi-omics and computer sciences, aiming to promote cancer prevention, screening, early diagnosis and precision treatment. The development of intelligent oncology has been facilitated by fast AI technology development such as natural language processing, machine/deep learning, computer vision, and robotic process automation. While the concept and applications of intelligent oncology is still in its infancy, and there are still many hurdles and challenges, we are optimistic that it will play a pivotal role for the future of basic, translational and clinical oncology.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Detecting pediatric wrist fractures using deep-learning-based object detection

BACKGROUND

Missed fractures are the leading cause of diagnostic error in the emergency department, and fractures of pediatric bones, particularly subtle wrist fractures, can be misidentified because of their varying characteristics and responses to injury.

OBJECTIVE

This study evaluated the utility of an object detection deep learning framework for classifying pediatric wrist fractures as positive or negative for fracture, including subtle buckle fractures of the distal radius, and evaluated the performance of this algorithm as augmentation to trainee radiograph interpretation.

MATERIALS AND METHODS

We obtained 395 posteroanterior wrist radiographs from unique pediatric patients (65% positive for fracture, 30% positive for distal radial buckle fracture) and divided them into train (n = 229), tune (n = 41) and test (n = 125) sets. We trained a Faster R-CNN (region-based convolutional neural network) deep learning object-detection model. Two pediatric and two radiology residents evaluated radiographs initially without the artificial intelligence (AI) assistance, and then subsequently with access to the bounding box generated by the Faster R-CNN model.

RESULTS

The Faster R-CNN model demonstrated an area under the curve (AUC) of 0.92 (95% confidence interval [CI] 0.87-0.97), accuracy of 88% (n = 110/125; 95% CI 81-93%), sensitivity of 88% (n = 70/80; 95% CI 78-94%) and specificity of 89% (n = 40/45, 95% CI 76-96%) in identifying any fracture and identified 90% of buckle fractures (n = 35/39, 95% CI 76-97%). Access to Faster R-CNN model predictions significantly improved average resident accuracy from 80 to 93% in detecting any fracture (P < 0.001) and from 69 to 92% in detecting buckle fracture (P < 0.001). After accessing AI predictions, residents significantly outperformed AI in cases of disagreement (73% resident correct vs. 27% AI, P = 0.002).

CONCLUSION

An object-detection-based deep learning approach trained with only a few hundred examples identified radiographs containing pediatric wrist fractures with high accuracy. Access to model predictions significantly improved resident accuracy in diagnosing these fractures.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives

Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.

Advances in biomarkers and techniques for pancreatic cancer diagnosis

Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Advancements in Oncology with Artificial Intelligence—A Review Article

Simple Summary

With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research.

Abstract

Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

Background

Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer.

Methods

A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis.

Results

Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725).

Conclusion

AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce.

Trial registration

PROSPERO CRD42020218004.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08773-w.

Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [18F]FP-CIT Positron Emission Tomography

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson's disease (PD) based on [¹⁸F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [¹⁸F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran's Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [¹⁸F]FP-CIT PET, and its performance was comparable to that of NM physicians.

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

Artificial intelligence in rectal cancer

Accurate and rapid diagnosis is essential for correct treatment in rectal cancer. Determining the optimal treatment plan for a patient with rectal cancer is a complex process, and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage. In recent years, the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology. Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer. It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician. Prediction algorithms can be standardized by sharing data between centers, diversifying data, and creating big data.

Artificial intelligence and colorectal cancer: How far can you go?

Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.

Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding

Tumor budding is considered a sign of cancer cell activity and the first step of tumor metastasis. This study aimed to establish an automatic diagnostic platform for rectal cancer budding pathology by training a Faster region-based convolutional neural network (F-R-CNN) on the pathological images of rectal cancer budding. Postoperative pathological section images of 236 patients with rectal cancer from the Affiliated Hospital of Qingdao University, China, taken from January 2015 to January 2017 were used in the analysis. The tumor site was labeled in Label image software. The images of the learning set were trained using Faster R-CNN to establish an automatic diagnostic platform for tumor budding pathology analysis. The images of the test set were used to verify the learning outcome. The diagnostic platform was evaluated through the receiver operating characteristic (ROC) curve. Through training on pathological images of tumor budding, an automatic diagnostic platform for rectal cancer budding pathology was preliminarily established. The precision–recall curves were generated for the precision and recall of the nodule category in the training set. The area under the curve = 0.7414, which indicated that the training of Faster R-CNN was effective. The validation in the validation set yielded an area under the ROC curve of 0.88, indicating that the established artificial intelligence platform performed well at the pathological diagnosis of tumor budding. The established Faster R-CNN deep neural network platform for the pathological diagnosis of rectal cancer tumor budding can help pathologists make more efficient and accurate pathological diagnoses.

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

BACKGROUND

Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging.

METHODS

We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis.

FINDINGS

We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning.

INTERPRETATION

AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field.

FUNDING

College students' innovative entrepreneurial training plan program .

The emerging role of super enhancer-derived noncoding RNAs in human cancer

Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.

A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer

Background

Preoperative diagnoses of metastatic lymph nodes (LNs) by the most advanced deep learning technology of Faster Region‐based Convolutional Neural Network (Faster R‐CNN) have not yet been reported.

Materials and Methods

In total, 545 patients with pathologically confirmed rectal cancer between January 2016 and March 2019 were included and were randomly allocated with a split ratio of 2:1 to the training and validation sets, respectively. The MRI images for metastatic LNs were evaluated by Faster R‐CNN. Multivariate regression analyses were used to develop the predictive models. Faster R‐CNN nomograms were constructed based on the multivariate analyses in the training sets and were validated in the validation sets.

Results

The Faster R‐CNN nomogram for predicting metastatic LN status contained predictors of age, metastatic LNs by Faster R‐CNN and differentiation degrees of tumors, with areas under the curves (AUCs) of 0.862 (95% CI: 0.816‐0.909) and 0.920 (95% CI: 0.876‐0.964) in the training and validation sets, respectively. The Faster R‐CNN nomogram for predicting LN metastasis degree contained predictors of metastatic LNs by Faster R‐CNN and differentiation degrees of tumors, with AUCs of 0.859 (95% CI: 0.804‐0.913) and 0.886 (95% CI: 0.822‐0.950) in the training and validation sets, respectively. Calibration plots and decision curve analyses demonstrated good calibrations and clinical utilities. The two nomograms were used jointly as a kit for predicting metastatic LNs.

Conclusion

The Faster R‐CNN nomogram kit exhibits excellent performance in discrimination, calibration, and clinical utility and is convenient and reliable for predicting metastatic LNs preoperatively.

Clinical trial registration: ChiCTR‐DDD‐17013842.

Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects

Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Emerging radiotherapy technologies and trends in nasopharyngeal cancer

Technology has always driven advances in radiotherapy treatment. In this review, we describe the main technological advances in radiotherapy over the past decades for the treatment of nasopharyngeal cancer (NPC) and highlight some of the pressing issues and challenges that remain. We aim to identify emerging trends in radiation medicine. These include advances in personalized medicine and advanced imaging modalities, standardization of planning and delineation, assessment of treatment response and adaptive re‐planning, impact of particle therapy, and role of artificial intelligence or automation in clinical care. In conclusion, we expect significant improvement in the therapeutic ratio of radiotherapy treatment for NPC over the next decade.

Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network

Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.

Differentiate cavernous hemangioma from schwannoma with artificial intelligence (AI)

Background

Cavernous hemangioma and schwannoma are tumors that both occur in the orbit. Because the treatment strategies of these two tumors are different, it is necessary to distinguish them at treatment initiation. Magnetic resonance imaging (MRI) is typically used to differentiate these two tumor types; however, they present similar features in MRI images which increases the difficulty of differential diagnosis. This study aims to devise and develop an artificial intelligence framework to improve the accuracy of clinicians' diagnoses and enable more effective treatment decisions by automatically distinguishing cavernous hemangioma from schwannoma.

Methods

Material: As the study materials, we chose MRI images as the study materials that represented patients from diverse areas in China who had been referred to our center from more than 45 different hospitals. All images were initially acquired on films, which we scanned into digital versions and recut. Finally, 11,489 images of cavernous hemangioma (from 33 different hospitals) and 3,478 images of schwannoma (from 16 different hospitals) were collected. Labeling: All images were labeled using standard anatomical knowledge and pathological diagnosis. Training: Three types of models were trained in sequence (a total of 96 models), with each model including a specific improvement. The first two model groups were eye- and tumor-positioning models designed to reduce the identification scope, while the third model group consisted of classification models trained to make the final diagnosis.

Results

First, internal four-fold cross-validation processes were conducted for all the models. During the validation of the first group, the 32 eye-positioning models were able to localize the position of the eyes with an average precision of 100%. In the second group, the 28 tumor-positioning models were able to reach an average precision above 90%. Subsequently, using the third group, the accuracy of all 32 tumor classification models reached nearly 90%. Next, external validation processes of 32 tumor classification models were conducted. The results showed that the accuracy of the transverse T1-weighted contrast-enhanced sequence reached 91.13%; the accuracy of the remaining models was significantly lower compared with the ground truth.

Conclusions

The findings of this retrospective study show that an artificial intelligence framework can achieve high accuracy, sensitivity, and specificity in automated differential diagnosis between cavernous hemangioma and schwannoma in a real-world setting, which can help doctors determine appropriate treatments.

Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study

BACKGROUND

Accurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI.

METHODS

In total, 5789 annotated LNs (diameter ≥ 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC).

FINDINGS

For LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81-0.82.

INTERPRETATION

This deep learning-based auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.

Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone

Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists' assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, "learning" the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance.

Real-time colorectal cancer diagnosis using PR-OCT with deep learning

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. Results: The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. Conclusions: Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned.

Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network

BACKGROUND

Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer. This study was performed to develop an automatic and accurate imaging processing technique system, allowing this system to read computed tomography (CT) images correctly and make diagnosis of pancreatic cancer faster.

METHODS

The establishment of the artificial intelligence (AI) system for pancreatic cancer diagnosis based on sequential contrast-enhanced CT images were composed of two processes: training and verification. During training process, our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set. Additionally, we used VGG16, which was pre-trained in ImageNet and contained 13 convolutional layers and three fully connected layers, to initialize the feature extraction network. In the verification experiment, we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network (Faster R-CNN) model that had completed training. Totally, 1699 images from 100 pancreatic cancer patients were included for clinical verification.

RESULTS

A total of 338 patients with pancreatic cancer were included in the study. The clinical characteristics (sex, age, tumor location, differentiation grade, and tumor-node-metastasis stage) between the two training and verification groups were insignificant. The mean average precision was 0.7664, indicating a good training effect of the Faster R-CNN. Sequential contrast-enhanced CT images of 100 pancreatic cancer patients were used for clinical verification. The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632. It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image, which is much faster than the time required for diagnosis by an imaging specialist.

CONCLUSIONS

Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.

TRIAL REGISTRATION

ChiCTR1800017542; http://www.chictr.org.cn.

Future of Radiotherapy in Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However, the further efficacy brought by radical radiotherapy has reached the bottleneck in advanced patients, who are prone to develop recurrence and distant metastasis after treatment. The application of photon therapy makes it possible for radiation dose escalation in refractory cases and may provide second chance for recurrent patients with less unrecoverable tissue damage. The concept of adaptive radiotherapy is put forward in consideration of target volume shrinkage during treatment. The replanning procedure offers better protection for the organ at risk. However, the best timing and candidates for adaptive radiotherapy is still under debate. The current tendency of artificial intelligence in NPC mainly focuses on image recognition, auto-segmentation and dose prediction. Although artificial intelligence is still in developmental stage, the future of it is promising.To further improve the efficacy of NPC, multimodality treatment is encouraged. In-depth studies on genetic and epigenetic variations help to explain the great heterogeneity among patients, and could further be applied to precise screening and prediction, personalized radiotherapy and the evolution of targeted drugs. Given the clinical benefit of immunotherapy in other cancers, the application of immunotherapy, especially immune checkpoint inhibitor, in NPC is also of great potential. Results from ongoing clinical trials combining immunotherapy with radiotherapy in NPC are expected.

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

BACKGROUND

An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use.

METHODS

Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome; recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model.

RESULTS

Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients.

CONCLUSION

Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.

TRIAL REGISTRATION

www.chictr.org.cn (No. ChiCTR-DDD-17013842).

Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy

Objective

To predict pathological nodal stage of locally advanced rectal cancer by a radiomic method that uses collective features of multiple lymph nodes (LNs) in magnetic resonance images before and after neoadjuvant chemoradiotherapy (NCRT).

Methods

A total of 215 patients were included in this study and chronologically divided into the discovery cohort (n=143) and validation cohort (n=72). In total, 2,931 pre-NCRT LNs and 1,520 post-NCRT LNs were delineated from all visible rectal LNs in magnetic resonance images. Geometric, first-order and texture features were extracted from each LN before and after NCRT. Collective features are defined as the maximum, minimum, mean, median value and standard deviation of each feature from all delineated LNs of each participant. LN-model is constructed from collective LN features by logistic regression model with L1 regularization to predict pathological nodal stage (ypN0 or ypN+). Tumor-model is constructed from tumor features for comparison by using DeLong test.

Results

The LN-model selects 7 features from 412 LN features, and the tumor-model selects 7 features from 82 tumor features. The area under the receiver operating characteristic curve (AUC) of LN-model in the discovery cohort is 0.818 [95% confidence interval (95% CI): 0.745−0.878], significantly (Z=2.09, P=0.037) larger than 0.685 (95% CI: 0.602−0.760) of the tumor-model. The AUC of LN-model in validation cohort is 0.812 (95% CI: 0.703−0.895), significantly (Z=3.106, P=0.002) larger than 0.517 (95% CI: 0.396−0.636) of the tumor-model.

Conclusions

The usage of collective features from all visible rectal LNs performs better than the usage of tumor features for the prediction of pathological nodal stage of locally advanced rectal cancer.