CT colonography: computer-aided detection of morphologically flat T1 colonic carcinoma

Big data and artificial intelligence in cancer research

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of 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.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

Laterally spreading tumors: Limitations of computed tomography colonography

AIM: To prospectively investigate the detection rate of laterally spreading tumors (LSTs) of the colorectum by computed tomography (CT) colonography (CTC).

METHODS: Patients with LSTs measuring ≥ 20 mm detected during colonoscopy were prospectively enrolled in the study. All patients underwent colonoscopy and subsequent CTC on the same day. CTC was performed using multi-detector CT without contrast in the prone and supine positions. Two radiologists blinded to the existence of LSTs read the virtual endoscopic images as well as 2-D images. LSTs were classified into granular and non-granular types based on colonoscopic appearance.

RESULTS: Forty-seven pathologically proven LSTs were evaluated prospectively. Histology included adenomas in 19, mucosal cancers in 19 and T1 cancers in 9. The mean diameter of the LSTs was 35.1 mm. Twenty-eight (60%) LSTs were correctly identified by CTC, and the configuration was similar to the colonoscopic appearance in most cases. Detection rate for the granular type was significantly higher than that for the non-granular type (71% vs 31%, P = 0.013). Detection rate of adenomas was significantly lower than mucosal cancers (32% vs 79%, P = 0.008) and T1 cancers (32% vs 78%, P = 0.042).

CONCLUSION: The detection rate of LSTs by CTC, particularly the non-granular type was not acceptable. Practitioners should be aware of the relatively low detection rate when using CTC.

Carpet lesions detected at CT colonography: clinical, imaging, and pathologic features

PURPOSE

To describe carpet lesions (laterally spreading tumors ≥ 3 cm) detected at computed tomographic (CT) colonography, including their clinical, imaging, and pathologic features.

MATERIALS AND METHODS

The imaging reports for 9152 consecutive adults undergoing initial CT colonography at a tertiary center were reviewed in this HIPAA-compliant, institutional review board-approved retrospective study to identify all potential carpet lesions detected at CT colonography. Carpet lesions were defined as morphologically flat, laterally spreading tumors 3 cm or larger. For those patients with neoplastic carpet lesions, CT colonography studies were analyzed to determine maximal lesion width and height, oral contrast material coating, segmental location, and computer-aided detection (CAD) findings. Demographic data and details of clinical treatment in these patients were reviewed.

RESULTS

Eighteen carpet lesions in 18 patients (0.2%; mean age, 67.1 years; eight men, 10 women) were identified and were subsequently confirmed at colonoscopy and pathologic examination among 20 potential flat masses (≥3 cm) prospectively identified at CT colonography (there were two nonneoplastic rectal false-positive findings). No additional neoplastic carpet lesions were found in the cohort undergoing colonoscopy after CT colonography and/or surgery (there were no false-negatives). Mean lesion width was 46.5 mm (range, 30-80 mm); mean lesion height was 7.9 mm (range, 4-14 mm). Surface retention of oral contrast material was noted in all 18 cases. All but two lesions were located in the distal rectosigmoid or proximal right colon. At CAD, 17 (94.4%) lesions were detected (mean, 6.2 CAD marks per lesion). Sixteen lesions (88.9%) demonstrated advanced histologic features, including a villous component (n = 11), high-grade dysplasia (n = 4), and invasive cancer (n = 5). Sixteen patients (88.9%) required surgical treatment for complete excision.

CONCLUSION

CT colonography can effectively depict carpet lesions. Common features in this series included older patient age, rectal or cecal location, surface coating with oral contrast material, multiple CAD hits, advanced yet typically benign histologic features, and surgical treatment.

Haustral fold segmentation with curvature-guided level set evolution

Human colon has complex structures mostly because of the haustral folds. The folds are thin flat protrusions on the colon wall, which complicate the shape analysis for computer-aided detection (CAD) of colonic polyps. Fold segmentation may help reduce the structural complexity, and the folds can serve as an anatomic reference for computed tomographic colonography (CTC). Therefore, in this study, based on a model of the haustral fold boundaries, we developed a level-set approach to automatically segment the fold surfaces. To evaluate the developed fold segmentation algorithm, we first established the ground truth of haustral fold boundaries by experts' drawing on 15 patient CTC datasets without severe under/over colon distention from two medical centers. The segmentation algorithm successfully detected 92.7% of the folds in the ground truth. In addition to the sensitivity measure, we further developed a merit of segmented-area ratio (SAR), i.e., the ratio between the area of the intersection and union of the expert-drawn folds and the area of the automatically segmented folds, to measure the segmentation accuracy. The segmentation algorithm reached an average value of SAR = 86.2%, showing a good match with the ground truth on the fold surfaces. We believe the automatically segmented fold surfaces have the potential to benefit many postprocedures in CTC, such as CAD, taenia coli extraction, supine-prone registration, etc.

Computer-aided detection of colorectal polyps in CT colonography with and without fecal tagging: a stand-alone evaluation

PURPOSE

To evaluate the stand-alone performance of a computer-aided detection (CAD) algorithm for colorectal polyps in a large heterogeneous CT colonography (CTC) database that included both tagged and untagged datasets.

METHODS

Written, informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective study. CTC datasets from 2063 patients were assigned to training (n = 374) and testing (n = 1689). The test set consisted of 836 untagged and 853 tagged examinations not used for CAD training. Examinations were performed at 15 sites in the United States, Asia, and Europe, using 4- to 64-multidetector-row computed tomography and various acquisition parameters. CAD sensitivities were calculated on a per-patient and per-polyp basis for polyps measuring ≥6 mm. The reference standard was colonoscopy in 1588 (94%) and consensus interpretation by expert radiologists in 101 (6%) patients. Statistical testing employed χ, logistic regression, and Mann-Whitney U tests.

RESULTS

In 383 of 1689 individuals, 564 polyps measuring ≥6 mm were identified by the reference standard (347 polyps: 6-9 mm and 217 polyps: ≥10 mm). Overall, CAD per-patient sensitivity was 89.6% (343/383), with 89.0% (187/210) for untagged and 90.2% (156/173) for tagged datasets (P = 0.72). Overall, per-polyp sensitivity was 86.9% (490/564), with 84.4% (270/320) for untagged and 90.2% (220/244) for tagged examinations (P = 068). The mean false-positive rate per patient was 5.14 (median, 4) in untagged and 4.67 (median, 4) in tagged patient datasets (P = 0.353).

CONCLUSION

Stand-alone CAD can be applied to both tagged and untagged CTC studies without significant performance differences. Detection rates are comparable to human readers at a relatively low false-positive rate, making CAD a useful tool in clinical practice.

CT virtual colonoscopy in displaying excavated colon lesions

The aim of this study was to evaluate the relative values of 2D plane view and 3D intracavity view of CT virtual colonoscopy in displaying colon excavated lesions. Cleaned porcine colon with ulcerative lesion was scanned with multidetector CT. The data were reconstructed and reviewed using 2D plane view and 3D volume-rendered images on a GE AW4.2 workstation. The 3D volume-rendered images showed superiority in displaying excavated lesions.

CT colonography: computer-assisted detection of colorectal cancer

OBJECTIVES

Computer-aided detection (CAD) for CT colonography (CTC) has been developed to detect benign polyps in asymptomatic patients. We aimed to determine whether such a CAD system can also detect cancer in symptomatic patients.

METHODS

CTC data from 137 symptomatic patients subsequently proven to have colorectal cancer were analysed by a CAD system at 4 different sphericity settings: 0, 50, 75 and 100. CAD prompts were classified by an observer as either true-positive if overlapping a cancer or false-positive if elsewhere. Colonoscopic data were used to aid matching.

RESULTS

Of 137 cancers, CAD identified 124 (90.5%), 122 (89.1%), 119 (86.9%) and 102 (74.5%) at a sphericity of 0, 50, 75 and 100, respectively. A substantial proportion of cancers were detected on either the prone or supine acquisition alone. Of 125 patients with prone and supine acquisitions, 39.3%, 38.3%, 43.2% and 50.5% of cancers were detected on a single acquisition at a sphericity of 0, 50, 75 and 100, respectively. CAD detected three cancers missed by radiologists at the original clinical interpretation. False-positive prompts decreased with increasing sphericity value (median 65, 57, 45, 24 per patient at values of 0, 50, 75, 100, respectively) but many patients were poorly prepared.

CONCLUSION

CAD can detect symptomatic colorectal cancer but must be applied to both prone and supine acquisitions for best performance.

Flat colon polyps: what should radiologists know?

With the recent publication of international computed tomography (CT) colonography standards, which aim to improve quality of examinations, this review informs radiologists about the significance of flat polyps (adenomas and hyperplastic polyps) in colorectal cancer pathways. We describe flat polyp classification systems and propose how flat polyps should be reported to ensure patient management strategies are based on polyp morphology as well as size. Indeed, consistency when describing flat polyps is of increasing importance given the strengthening links between CT colonography and endoscopy.

Effect of computer-aided detection for CT colonography in a multireader, multicase trial

PURPOSE

To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images.

MATERIALS AND METHODS

The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses.

RESULTS

Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05).

CONCLUSION

Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount.

CT colonography and non-polypoid colorectal neoplasms

Computed tomographic colonography (CTC) has been reported to be as effective as optical colonoscopy in the detection of significant adenomas. However, there are widely conflicting performance data in relation to detection of flat neoplasia. This article describes the potential and limitations of CTC and computer-aided diagnosis in the detection of flat neoplasms.

Flat (nonpolypoid) colorectal lesions identified at CT colonography in a U.S. screening population

RATIONALE AND OBJECTIVES

The aim of this study was to investigate the clinical importance and height definition of flat (nonpolypoid) colorectal lesions detected on screening computed tomographic colonography (CTC).

MATERIALS AND METHODS

Results from prospective screening CTC in 5107 consecutive asymptomatic adults (mean age, 56.9 years) at a single center were analyzed. All detected colorectal lesions > or = 6 mm were prospectively categorized as polypoid or flat (nonpolypoid). The maximal height of all flat lesions was measured to assess the suggested 3-mm threshold definition.

RESULTS

Of 954 polyps measuring > or = 6 mm identified on screening CTC, 125 lesions (13.1%) in 106 adults were prospectively categorized as flat, with a mean size of 12.7 mm (range, 6-80 mm), including 73 lesions 6 to 9 mm, 42 lesions 10 to 29 mm, and 10 lesions > or = 3 cm (carpet lesions). For polyps between 6 and 30 mm in size, flat lesions were less likely than polypoid lesions to be neoplastic (25.0% vs 60.3%, P < .001), histologically advanced (5.4% vs 12.1%, P = .07) or malignant (0% vs 0.5%, P = NS). Two of 10 carpet lesions (20%) were malignant, compared to 50% of polypoid masses > or = 3 cm. Of nine flat lesions seen only on colonoscopy (false-negatives on CTC), two were neoplastic (tubular adenomas), and none was histologically advanced. For all flat lesions between 6 and 30 mm, the maximal height averaged 2.2 mm and was < or =3 mm in 86.1%, including 93.2% of small 6-mm to 9-mm flat lesions.

CONCLUSION

In a US screening population, flat colorectal lesions detected on CTC demonstrated less aggressive histologic features compared to polypoid lesions. Excluding carpet lesions, a maximal height of 3 mm appears to be a reasonable definition.

Automated computer-aided stenosis detection at coronary CT angiography: initial experience

OBJECTIVE

To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).

METHODS

We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.

RESULTS

QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.

CONCLUSION

Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.

Sensitivity of CT colonography for nonpolypoid colorectal lesions interpreted by human readers and with computer-aided detection

OBJECTIVE

The purpose of our study was to determine the sensitivity of CT colonography (CTC) interpreted by human readers and with computer-aided detection (CAD) for genuinely nonpolypoid colorectal lesions, defined as 2 mm or less in lesion height at colonoscopy.

MATERIALS AND METHODS

A computerized database search for a 33-month period found 21 patients who had undergone both colonoscopy and CTC and who had a total of 23 genuinely nonpolypoid colorectal lesions: eight adenomas (9-30 mm in width), 10 stage Tis or T1 adenocarcinomas (10-25 mm), and five nonadenomatous lesions (8-20 mm). CTC was performed using a cathartic preparation and fecal tagging and was interpreted by experienced readers in a blinded manner using a primary 3D method and with CAD.

RESULTS

The sensitivities of human readers for nonpolypoid adenomatous lesions (i.e., both adenomas and adenocarcinomas), adenocarcinomas, and nonadenomatous lesions were 66.7% (12/18), 90% (9/10), and 0% (0/5), respectively. Sensitivities were 55.6% (10/18), 90% (9/10), and 0% (0/5) for CAD. A 10-mm stage T1 adenocarcinoma was missed by a human reader on blinded review but was detected with CAD. Both human readers and CAD yielded significantly higher sensitivity for adenomatous lesions than for nonadenomatous lesions (p = 0.014 and 0.046, respectively) and for adenocarcinomas than for noncancerous lesions (p = 0.003 and 0.0001, respectively).

CONCLUSION

CTC showed a high sensitivity for nonpolypoid stage Tis and T1 adenocarcinomas 10 mm or greater in width despite the limited overall sensitivity for nonpolypoid adenomatous lesions, when performed using cathartic preparation and fecal tagging.

Influence of computer-aided detection false-positives on reader performance and diagnostic confidence for CT colonography

OBJECTIVE

The objective of our study was to investigate whether an increasing number of computer-aided detection (CAD) false-positives decreases reader sensitivity, specificity, and confidence for nonexpert readers of CT colonography (CTC).

MATERIALS AND METHODS

Fifty CTC data sets (29 men; mean age, 65 years), 25 of which contained 35 polyps > or = 5 mm, were selected in which CAD had 100% polyp sensitivity at two sphericity settings (0 and 75) but differed in the number of false-positives. The data sets were read by five readers twice: once at each sphericity setting. Sensitivity, specificity, report time, and confidence before and after second-read CAD were compared using the paired exact and Student's t test, respectively. Receiver operating characteristic (ROC) curves were generated using reader confidence (1-100) in correct case classification (normal or abnormal).

RESULTS

CAD generated a mean of 42 (range, 3-118) and 15 (range, 1-36) false-positives at a sphericity of 0 and 75, respectively. CAD at both settings increased per-patient sensitivity from 82% to 87% (p = 0.03) and per-polyp sensitivity by 8% and 10% for a sphericity of 0 and 75, respectively (p < 0.001). Specificity decreased from 84% to 79% (sphericity 0 and 75, p = 0.03 and 0.07). There was no difference in sensitivity, specificity, or reader confidence between sphericity settings (p = 1.0, 1.0, 0.11, respectively). The area under the ROC curve was 0.78 (95% CI, 0.70-0.86) and 0.77 (0.68-0.85) for a sphericity of 0 and 75, respectively. CAD added a median of 4.4 minutes (interquartile range [IQR], 2.7-6.5 minutes) and 2.2 minutes (IQR, 1.2-4.0 minutes) for a sphericity of 0 and 75, respectively (p < 0.001). CONCLUSION. CAD has the potential to increase the sensitivity of readers inexperienced with CTC, although specificity may be reduced. An increased number of CAD-generated false-positives does not negate any beneficial effect but does reduce efficiency.