The insufficiency of CT examination in early detection of central lung squamous cell carcinoma and squamous epithelial precancerous lesions.

BACKGROUND: CT examination for lung cancer has been carried out for more than 20 years and great achievements have been made in the early detection of lung cancer. However, in the clinical work, a large number of advanced central lung squamous cell carcinoma are still detected through bronchoscopy. Meanwhile, a part of CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions are also accidentally detected through bronchoscopy. METHODS: This study retrospectively collects the medical records of patients in the bronchoscopy room of the Endoscopy Department of Zhejiang Cancer Hospital from January 2014 to December 2018. The inclusion criteria for patients includes: 1.Patient medical records completed, 2.Without history of lung cancer before the diagnosis and first pathological diagnosis of primary lung cancer, 3.Have the lung CT data of the same period, 4.Have the bronchoscopy records and related pathological diagnosis, 5.The patients undergoing radical surgical treatment must have a complete postoperative pathological diagnosis. Finally, a total of 10,851 patients with primary lung cancer are included in the study, including 7175 males and 3676 females, aged 22-98 years. Firstly, 130 patients with CT-occult lesions are extracted and their clinical features are analyzed. Then, 604 cases of single central squamous cell carcinoma and 3569 cases of peripheral adenocarcinoma are extracted and compares in postoperative tumor diameter and lymph node metastasis. RESULTS: 115 cases of CT-occult central lung squamous cell carcinoma and 15 cases of squamous epithelial precancerous lesions are found. In the total lung cancer, the proportion of CT-occult lesions is 130/10,851 (1.20%). Meanwhile, all these patients are middle-aged and elderly men with a history of heavy smoking. There are statistically significant differences in postoperative median tumor diameter (3.65 cm vs.1.70 cm, P < 0.0001) and lymph node metastasis rate (50.99% vs.13.06%, P < 0.0001) between 604 patients with operable single central lung squamous cell carcinoma and 3569 patients with operable peripheral lung adenocarcinoma. Of the 604 patients with squamous cell carcinoma, 96.52% (583/604) are male with a history of heavy smoking and aged 40-82 years with a median age of 64 years. CONCLUSIONS: This study indicates that the current lung CT examination of lung cancer is indeed insufficiency for the early diagnosis of central squamous cell carcinoma and squamous epithelial precancerous lesions. Further bronchoscopy in middle-aged and elderly men with a history of heavy smoking can make up for the lack of routine lung CT examination.

Analyzing risk factors and constructing a predictive model for superficial esophageal carcinoma with submucosal infiltration exceeding 200 micrometers.

OBJECTIVE: Submucosal infiltration of less than 200 µm is considered an indication for endoscopic surgery in cases of superficial esophageal cancer and precancerous lesions. This study aims to identify the risk factors associated with submucosal infiltration exceeding 200 micrometers in early esophageal cancer and precancerous lesions, as well as to establish and validate an accompanying predictive model. METHODS: Risk factors were identified through least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Various machine learning (ML) classification models were tested to develop and evaluate the most effective predictive model, with Shapley Additive Explanations (SHAP) employed for model visualization. RESULTS: Predictive factors for early esophageal invasion into the submucosa included endoscopic ultrasonography or magnifying endoscopy> SM1(P<0.001,OR = 3.972,95%CI 2.161-7.478), esophageal wall thickening(P<0.001,OR = 12.924,95%CI,5.299-33.96), intake of pickled foods(P=0.04,OR = 1.837,95%CI,1.03-3.307), platelet-lymphocyte ratio(P<0.001,OR = 0.284,95%CI,0.137-0.556), tumor size(P<0.027,OR = 2.369,95%CI,1.128-5.267), the percentage of circumferential mucosal defect(P<0.001,OR = 5.286,95%CI,2.671-10.723), and preoperative pathological type(P<0.001,OR = 4.079,95%CI,2.254-7.476). The logistic regression model constructed from the identified risk factors was found to be the optimal model, demonstrating high efficacy with an area under the curve (AUC) of 0.922 in the training set, 0.899 in the validation set, and 0.850 in the test set. CONCLUSION: A logistic regression model complemented by SHAP visualizations effectively identifies early esophageal cancer reaching 200 micrometers into the submucosa.

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening.

BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy. METHODS: This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent). RESULTS: The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other. CONCLUSION: Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future.

Narrow band imaging reveals field cancerisation undetected by conventional white light: Optical diagnosis versus histopathology.

OBJECTIVE: To assess whether narrow band imaging (NBI) detects fields of cancerisation around suspicious lesions in the upper aerodigestive tract, which were undetected by white light imaging (WLI). METHODS: In 96 patients with laryngeal and pharyngeal lesions suspicious for malignancy, 206 biopsies were taken during laryngoscopy: 96 biopsies of suspicious lesions detected by both WLI and NBI (WLI+/NBI+), 60 biopsies adjacent mucosa only suspicious with NBI (WLI-/NBI+), and 46 biopsies of NBI and WLI unsuspicious mucosa (WLI-/NBI-) as negative controls. Optical diagnosis according to the Ni-classification was compared with histopathology. RESULTS: Signs of (pre)malignancy were found in 88% of WLI+/NBI+ biopsies, 32% of WLI-/NBI+ biopsies and 0% in WLI-/NBI- (p < .001). In 58% of the WLI-/NBI+ mucosa any form of dysplasia or carcinoma was detected. CONCLUSION: The use of additional NBI led to the detection of (pre)malignancy in 32% of the cases, that would have otherwise remained undetected with WLI alone. This highlights the potential of NBI as a valuable adjunct to WLI in the identification of suspicious lesions in the upper aerodigestive tract.

Surgical treatment of breast precancers - our experience.

INTRODUCTION: Thanks to mammographic screening and the improvement of breast cancer diagnostics, the detection of precancers is also increasing. They are defined as morphological changes of the mammary gland which are more likely to cause cancer. The evaluated precancers are atypical ductal hyperplasia (ADH), lobular carcinoma in situ (LCIS) and radial scar. METHODOLOGY: In the period 1. 1. 2018-31. 12. 2022, we performed 1,302 planned operations for breast disease at the Surgical Clinic of Teaching Hospital Plzen, of which 30 (2%) were precancer operations. ADH was confirmed 11×, LCIS 8×, and a radical scar 11×. The average age of the patients in all three groups was 56 years (27-85). Precancer was diagnosed 8× only by sonography, 3× by mammography and 19× by a combination of both methods. Subsequently, a puncture biopsy was always completed. We performed 28 tumor excisions with intraoperative biopsy and 2 mastectomies. RESULTS: In the case of ADH from puncture biopsy, ADH was confirmed intraoperatively 8×, DCIS was diagnosed 2×, and mucinous carcinoma 1×. In LCIS, no tumor was found by intraoperative biopsy 4×, LCIS was confirmed 1×, lobular invasive carcinoma was diagnosed 1×, mastectomy was performed 2× without intraoperative biopsy. In the radial scar, ADH was diagnosed 3×, sclerosing adenosis 6×, DCIS 1×, invasive carcinoma 1×. After the final histological processing of the samples, there was an increase in diagnosed carcinomas. In ADH, DCIS was confirmed 3×, DIC 2×, and mucinous carcinoma 1×. In LCIS, LIC was diagnosed 3×. In the radial scar, DCIS was confirmed 1×, and invasive carcinoma remain 1×. Thus, carcinoma was diagnosed in 11 patients (37%) thanks to the surgical solution. No patient underwent axillary node surgery. All 11 patients subsequently underwent oncological treatment, always a combination of radiotherapy and hormone therapy. All patients are alive, 10 patients are in complete remission of the disease, one with DCIS experienced a local recurrence after 4 years. CONCLUSION: Surgical treatment of precancers of the breast makes sense, DCIS or even invasive cancer is often hidden in addition to precancer. Thanks to the surgical solution, the cancer was detected in time.

Timeline of Development of Pancreatic Cancer and Implications for Successful Early Detection in High-Risk Individuals.

BACKGROUND & AIMS: To successfully implement imaging-based pancreatic cancer (PC) surveillance, understanding the timeline and morphologic features of neoplastic progression is key. We aimed to investigate the progression to neoplasia from serial prediagnostic pancreatic imaging tests in high-risk individuals and identify factors associated with successful early detection. METHODS: We retrospectively examined the development of pancreatic abnormalities in high-risk individuals who were diagnosed with PC or underwent pancreatic surgery, or both, in 16 international surveillance programs. RESULTS: Of 2552 high-risk individuals under surveillance, 28 (1%) developed neoplastic progression to PC or high-grade dysplasia during a median follow-up of 29 months after baseline (interquartile range [IQR], 40 months). Of these, 13 of 28 (46%) presented with a new lesion (median size, 15 mm; range 7-57 mm), a median of 11 months (IQR, 8; range 3-17 months) after a prior examination, by which time 10 of 13 (77%) had progressed beyond the pancreas. The remaining 15 of 28 (54%) had neoplastic progression in a previously detected lesion (12 originally cystic, 2 indeterminate, 1 solid), and 11 (73%) had PC progressed beyond the pancreas. The 12 patients with cysts had been monitored for 21 months (IQR, 15 months) and had a median growth of 5 mm/y (IQR, 8 mm/y). Successful early detection (as high-grade dysplasia or PC confined to the pancreas) was associated with resection of cystic lesions (vs solid or indeterminate lesions (odds ratio, 5.388; 95% confidence interval, 1.525-19.029) and small lesions (odds ratio, 0.890/mm; 95% confidence interval 0.812-0.976/mm). CONCLUSIONS: In nearly half of high-risk individuals developing high-grade dysplasia or PC, no prior lesions are detected by imaging, yet they present at an advanced stage. Progression can occur before the next scheduled annual examination. More sensitive diagnostic tools or a different management strategy for rapidly growing cysts are needed.

Magnifying image-enhanced endoscopy-only mode boosted early cancer diagnostic efficiency: a multicenter randomized controlled trial.

BACKGROUND AND AIMS: Magnifying image-enhanced endoscopy (MIEE) is an advanced endoscopy with image enhancement and magnification used in preoperative examination. However, its impact on the detection rate is unknown. METHODS: We conducted an open-label, randomized, parallel (1:1:1), controlled trial in 6 hospitals in China. Patients were recruited between February 14, 2022 and July 30, 2022. Eligible patients were aged ≥18 years and undergoing gastroscopy in outpatient departments. Participants were randomly assigned to the MIEE-only mode (o-MIEE) group, white-light endoscopy-only mode (o-WLE) group, and MIEE when necessary mode (n-MIEE) group (initial WLE followed by switching to another endoscope with MIEE if necessary). Biopsy sampling of suspicious lesions of the lesser curvature of the gastric antrum was performed. Primary and secondary aims were to compare detection rates and positive predictive value (PPV) of early cancer and precancerous lesions in these 3 modes, respectively. RESULTS: A total of 5100 recruited patients were randomly assigned to the o-MIEE (n = 1700), o-WLE (n = 1700), and n-MIEE (n = 1700) groups. In the o-MIEE, o-WLE, and n-MIEE groups, 29 (1.51%; 95% confidence interval [CI], 1.05-2.16), 4 (.21%; 95% CI, .08-.54), and 8 (.43%; 95% CI, .22-.85) early cancers were found, respectively (P < .001). The PPV for early cancer was higher in the o-MIEE group compared with the o-WLE and n-MIEE groups (63.04%, 33.33%, and 38.1%, respectively; P = .062). The same trend was seen for precancerous lesions (36.67%, 10.00%, and 21.74%, respectively). CONCLUSIONS: The o-MIEE mode resulted in a significant improvement in diagnosing early upper GI cancer and precancerous lesions; thus, it could be used for opportunistic screening. (Clinical trial registration number: ChiCTR2200064174.).

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video).

BACKGROUND AND AIMS: Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. METHODS: Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. RESULTS: The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. CONCLUSIONS: The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

Update on Lobular Neoplasia.

Lobular neoplasia (LN) is a histopathologic entity that encompasses both lobular carcinoma in situ (LCIS) and atypical lobular hyperplasia (ALH). Management of LN is known to be variable and institutionally dependent. The variability in approach after a diagnosis of LN at percutaneous breast biopsy derives in part from heterogeneity in the literature, resulting in a range of reported upgrade rates to malignancy after initial identification at percutaneous biopsy, and also from historical shifts in understanding of the natural history of LN. It has become increasingly recognized that not all LN is the same and that distinct variants of LN such as pleomorphic LCIS and florid LCIS have distinct natural histories and distinct likelihoods of upgrade to malignancy. In addition, it is also increasingly understood that appropriate management of LN relies on scrupulous radiologic-pathologic correlation. This review details the imaging features and histopathologic nature of ALH, classic-type LCIS, and the LCIS variants; addresses changes in the historical understanding of this entity contributing to confusion regarding its management; and discusses the importance of performing radiologic-pathologic correlation after percutaneous biopsy to help guide appropriate management steps when LN is encountered. In addition to the short-term implications of an LN diagnosis in terms of upgrade and surgical outcomes, the long-term implications of an LN diagnosis regarding risk of developing a later breast cancer are examined. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Magnifying endoscopy with narrow-band imaging for diagnosis of subtype of gastric intestinal metaplasia.

BACKGROUND AND AIM: Patients with incomplete gastric intestinal metaplasia (GIM) have a higher risk of gastric cancer (GC) than those with complete GIM. We aimed to clarify whether micromucosal patterns of GIM in magnifying endoscopy with narrow-band imaging (M-NBI) were useful for diagnosis of incomplete GIM. METHODS: We enrolled patients with a history of endoscopic resection of GC or detailed inspection for suspicious or definite GC. The antrum greater curvature and corpus lesser curvature were regions of interest. Areas with endoscopic findings of light blue crest and/or white opaque substance (WOS) were defined as endoscopic GIM, and subsequent M-NBI was applied. Micromucosal patterns were classified into Foveola and Groove types, and targeted biopsies were performed on GIM with each pattern. GIM was classified into complete and incomplete types using mucin (MUC)2, MUC5AC, MUC6, and CD10 immunohistochemical staining. The primary endpoint was the association between micromucosal pattern and histological subtype. The secondary endpoint was endoscopic findings associated with incomplete GIM. RESULTS: We analyzed 98 patients with 156 GIMs. Univariate analysis (odds ratio [OR] 3.4, P = 0.004), but not multivariate analysis (OR 0.87, P = 0.822), demonstrated a significant association between micromucosal pattern and subtype. The antrum (OR 3.7, P = 0.006) and WOS (OR 43, P = 0.002) were independent predictors for incomplete GIM. The WOS had 69% sensitivity and 93% specificity. CONCLUSIONS: The M-NBI micromucosal pattern is not useful for diagnosis of GIM subtype. WOS is a promising endoscopic indicator for diagnosis of incomplete GIM. (UMIN-CTR000041119).

Endoscopic resection of gastric low-grade dysplasia with high risk factors is associated with decreased advanced neoplasia: a single-center retrospective cohort study.

BACKGROUND: The natural course of gastric low-grade dysplasia (LGD) remains unclear, and there are inconsistent management recommendations among guidelines and consensus. OBJECTIVE: This study aimed to investigate the incidence of advanced neoplasia in patients with gastric LGD and identify the related risk factors. METHODS: Cases of biopsy demonstrated LGD (BD-LGD) at our center from 2010 to 2021 were reviewed retrospectively. Risk factors related to histological progression were identified, and outcomes of patients based on risk stratification were evaluated. RESULTS: Ninety-seven (23.0%) of 421 included BD-LGD lesions were diagnosed as advanced neoplasia. Among 409 superficial BD-LGD lesions, lesion in the upper third of the stomach, H. pylori infection, larger size, and narrow band imaging (NBI)-positive findings were independent risk factors of progression. NBI-positive lesions and NBI-negative lesions with or without other risk factors had 44.7%, 1.7%, and 0.0% risk of advanced neoplasia, respectively. Invisible lesions, visible lesions (VLs) without a clear margin, and VLs with a clear margin and size ≤ 10 mm, or > 10 mm had 4.8%, 7.9%, 16.7%, and 55.7% risk of advanced neoplasia, respectively. In addition, endoscopic resection decreased the risk of cancer (P < 0.001) and advanced neoplasia (P < 0.001) in patients with NBI-positive lesions, but not in NBI-negative patients. Similar results were found in patients with VLs with clear margin and size > 10 mm. Moreover, NBI-positive lesions had higher sensitivity and lower specificity for predicting advanced neoplasia than VLs with a clear margin and size > 10 mm determined by white-light endoscopy (97.6% vs. 62.7%, P < 0.001; and 63.0% vs. 85.6%, P < 0.001, respectively). CONCLUSION: Progression of superficial BD-LGD is associated with NBI-positive lesions, as well as with VLs with a clear margin (size > 10 mm) if NBI is unavailable, and selective resection of those lesions offers benefits for patients by decreasing the risk of advanced neoplasia.

The predictive value of the change of the number of pixels under different CT value intervals in the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions.

BACKGROUND: Due to the fact that the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions. (CT-occult CLSCC and SEPL) cannot be detected by lung CT screening, early and timely diagnosis of central lung cancer becomes very difficult, which directly affects the prognosis of patients. METHODS: We retrospectively review medical records of patients at the Zhejiang Cancer Hospital and enrolled 41 patients with the CT-occult CLSCC and SEPL and 48 patients without the CT-occult CLSCC and SEPL. We compare the clinical characteristics, imaging features and Changes in the number of pixels under different CT value intervals of patients with and without the CT-occult CLSCC and SEPL and we perform univariate and multivariate logistic regression analysis to explore independent factors for the CT-occult CLSCC and SEPL in the patients. RESULTS: We demonstrate that pack-years ≥ 20 (OR: 3.848, 95% CI: 1.086 ~ 13.633), the number of pixels change of CT value in interval [-850 ~ -750HU] (OR: 5.302, 95% CI: 1.122 ~ 25.057) and in interval [-900 ~ -850HU] (OR: 3.478, 95% CI: 1.167 ~ 10.365) are independently associated with the CT-occult CLSCC and SEPL in the patients. Ultimately, the logistic model obtained is statistically significant (p < 0.05) and an area under the ROC curve is 0.776 (95% CI: 0.682-0.870). The sensitivity of this model is 90.2% and the specificity is 52.1%. CONCLUSION: The results of this study indicate that in the CT value range [-950 ~ -750HU], when the total number of lung pixels tend to increase towards the region with high CT value, the probability of the occurrence of CT-occult CLSCC and SEPL lesions also increases. Meanwhile, these results have guiding significance for the further study of radiomic.

Quantitative Evaluation of the Normal Cervix, Cervical Cancer, and Cervical Precancerous Changes Via Real-Time Shear Wave Elastography.

OBJECTIVES: The present study aims to evaluate the clinical application values of ultrasound real-time shear wave elastography (SWE) in the diagnosis and differential diagnosis of cervical cancer (CC). METHODS: A total of 285 married female patients were screened and divided into three groups according to the results of the pathological examination and the cervical ThinPrep cytologic test: 1) the CC group (n = 94); 2) the cervical intraepithelial neoplasia (CIN) group (n = 91); and 3) the normal control group (n = 100). The maximum Young's modulus (Emax), mean Young's modulus (Emean), minimum Young's modulus (Emin), and Young's modulus stability (Esd) in each group were measured and statistically analyzed. RESULTS: There were no statistically significant differences in Emax, Emean, Emin, and Esd values between the anterior and posterior cervical walls, premenopausal and postmenopausal women, and nonparturient and parturient women in the normal control group. The Emax, Emean, Emin, and Esd values in the CIN group showed no statistically significant differences in different periods when compared with the control group. The differences between the normal control group and the CC group were statistically significant; the CC group showed no statistically significant differences in Emax, Emean, Emin, and Esd values at different clinical stages and in different pathological types. The cutoff value of Emax for CC diagnosis, which was of the highest accuracy (89.7%), was 43.48 kpa. CONCLUSION: Ultrasound real-time SWE can be applied to CC diagnosis.

Image-enhanced endoscopy in the diagnosis of gastric premalignant conditions and gastric cancer. / Técnicas de imagen avanzada en la detección y caracterización de las lesiones precursoras del cáncer gástrico y del cáncer gástrico precoz.

Diagnosis of early gastric cancer and its precancerous lesions remains a challenge for great part of western endoscopists. Changes seen in the mucosal pattern are generally subtle and hence difficult to identify. In this article, we will review the usefulness of conventional and virtual chromoendoscopy and magnification endoscopy in the recognition and classification of these lesions.

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps.

OBJECTIVES: To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning. METHODS: In this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++. RESULTS: The training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue. CONCLUSIONS: In this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader. KEY POINTS: • Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans. • Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts. • Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6-9 mm size.

Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study.

BACKGROUND: Intrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC. METHODS: Images of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists. RESULTS: A total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated. CONCLUSIONS: The proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography.

BACKGROUND AND OBJECTIVES: Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. MATERIALS AND METHODS: Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. RESULTS: A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. CONCLUSION: It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy.

Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low-middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general clinicians, nurses, or midwives (called medical workers). However, not being able to recognize the significant pathophysiology of human papilloma virus (HPV) infection in terms of the columnar epithelial-cell, squamous epithelial-cell, and white-spot regions with abnormal blood vessels may be further aggravated by VIA screening, which achieves a wide range of sensitivity (49-98%) and specificity (75-91%); this might lead to a false result and high interobserver variances. Hence, the automated detection of the columnar area (CA), subepithelial region of the squamocolumnar junction (SCJ), and acetowhite (AW) lesions is needed to support an accurate diagnosis. This study proposes a mask-RCNN architecture to simultaneously segment, classify, and detect CA and AW lesions. We conducted several experiments using 262 images of VIA+ cervicograms, and 222 images of VIA-cervicograms. The proposed model provided a satisfactory intersection over union performance for the CA of about 63.60%, and AW lesions of about 73.98%. The dice similarity coefficient performance was about 75.67% for the CA and about 80.49% for the AW lesion. It also performed well in cervical-cancer precursor-lesion detection, with a mean average precision of about 86.90% for the CA and of about 100% for the AW lesion, while also achieving 100% sensitivity and 92% specificity. Our proposed model with the instance segmentation approach can segment, detect, and classify cervical-cancer precursor lesions with satisfying performance only from a VIA cervicogram.

[Establishment and clinical validation of an artificial intelligence YOLOv51 model for the detection of precancerous lesions and superficial esophageal cancer in endoscopic procedure].

Objective: To construct the diagnostic model of superficial esophageal squamous cell carcinoma (ESCC) and precancerous lesions in endoscopic images based on the YOLOv5l model by using deep learning method of artificial intelligence to improve the diagnosis of early ESCC and precancerous lesions under endoscopy. Methods: 13, 009 endoscopic esophageal images of white light imaging (WLI), narrow band imaging (NBI) and lugol chromoendoscopy (LCE) were collected from June 2019 to July 2021 from 1, 126 patients at the Cancer Hospital, Chinese Academy of Medical Sciences, including low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, ESCC limited to the mucosal layer, benign esophageal lesions and normal esophagus. By computerized random function method, the images were divided into a training set (11, 547 images from 1, 025 patients) and a validation set (1, 462 images from 101 patients). The YOLOv5l model was trained and constructed with the training set, and the model was validated with the validation set, while the validation set was diagnosed by two senior and two junior endoscopists, respectively, to compare the diagnostic results of YOLOv5l model and those of the endoscopists. Results: In the validation set, the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the YOLOv5l model in diagnosing early ESCC and precancerous lesions in the WLI, NBI and LCE modes were 96.9%, 87.9%, 98.3%, 88.8%, 98.1%, and 98.6%, 89.3%, 99.5%, 94.4%, 98.2%, and 93.0%, 77.5%, 98.0%, 92.6%, 93.1%, respectively. The accuracy in the NBI model was higher than that in the WLI model (P<0.05) and lower than that in the LCE model (P<0.05). The diagnostic accuracies of YOLOv5l model in the WLI, NBI and LCE modes for the early ESCC and precancerous lesions were similar to those of the 2 senior endoscopists (96.9%, 98.8%, 94.3%, and 97.5%, 99.6%, 91.9%, respectively; P>0.05), but significantly higher than those of the 2 junior endoscopists (84.7%, 92.9%, 81.6% and 88.3%, 91.9%, 81.2%, respectively; P<0.05). Conclusion: The constructed YOLOv5l model has high accuracy in diagnosing early ESCC and precancerous lesions in endoscopic WLI, NBI and LCE modes, which can assist junior endoscopists to improve diagnosis and reduce missed diagnoses.

Alveolar adenoma and coexisting atypical adenomatous hyperplasia: a case report and literature review.

Alveolar adenoma is a rare tumour of the lung. It is typically found in asymptomatic adults as a peripheral or subplerual nodule on imaging examination. Microscopically, the tumour is composed of admixture of epithelial and mesenchymal component in variable sized cystic or alveolar structures. The tumour shows a benign nature. There have been no reported recurrences or metastases. Malignant transformation of alveolar adenoma and coexisting with lung carcinoma have been rarely described. In this article, we report a case of an alveolar adenoma and coexisting atypical adenomatous hyperplasia. This case, contributing to the limited numbers of cases described to date, illustrates the importance of awareness on the possibility of alveolar adenoma being associated with lung carcinoma and its precursor lesions especially when diagnosed by small biopsy specimens.