Assessment of indeterminate pulmonary nodules detected in lung cancer screening: Diagnostic accuracy of FDG PET/CT

Tri-Compartmental Restriction Spectrum Imaging Based on 18F-FDG PET/MR for Identification of Primary Benign and Malignant Lung Lesions

BACKGROUND

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions.

OBJECTIVE

To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy.

STUDY TYPE

Prospective.

POPULATION

137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years).

FIELD STRENGTH/SEQUENCE

T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system.

ASSESSMENT

The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusionf 1 $$ {f}_1 $$ , hindered diffusionf 2 $$ {f}_2 $$ , and free diffusionf 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant.

RESULTS

Thef 1 $$ {f}_1 $$ , SUVmax were significantly higher, andf 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC (f 3 $$ {f}_3 $$ ) > AUC (f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC (f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging.

CONCLUSION

RSI-derived parameters (f 1 $$ {f}_1 $$ ,f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility off 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy.

LEVEL OF EVIDENCE: 2

TECHNICAL EFFICACY

Stage 2.

Does FDG-PET/CT for incidentally found pulmonary lesions lead to a cascade of more incidental findings?

OBJECTIVE

To determine the frequency, nature, and downstream healthcare costs of new incidental findings that are found on whole-body FDG-PET/CT in patients with a non-FDG-avid pulmonary lesion ≥10 mm that was incidentally found on previous imaging.

MATERIALS AND METHODS

This retrospective study included a consecutive series of patients who underwent whole-body FDG-PET/CT because of an incidentally found pulmonary lesion ≥10 mm.

RESULTS

Seventy patients were included, of whom 23 (32.9 %) had an incidentally found pulmonary lesion that proved to be non-FDG-avid. In 12 of these 23 cases (52.2 %) at least one new incidental finding was discovered on FDG-PET/CT. The total number of new incidental findings was 21, of which 7 turned out to be benign, 1 proved to be malignant (incurable metastasized cancer), and 13 whose nature remained unclear. One patient sustained permanent neurologic impairment of the left leg due to iatrogenic nerve damage during laparotomy for an incidental finding which turned out to be benign. The total costs of all additional investigations due to the detection of new incidental findings amounted to €9903.17, translating to an average of €141.47 per whole-body FDG-PET/CT scan performed for the evaluation of an incidentally found pulmonary lesion.

CONCLUSION

In many patients in whom whole-body FDG-PET/CT was performed to evaluate an incidentally found pulmonary lesion that turned out to be non-FDG-avid and therefore very likely benign, FDG-PET/CT detected new incidental findings in our preliminary study. Whether the detection of these new incidental findings is cost-effective or not, requires further research with larger sample sizes.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.

Value of subspecialist second opinion reads of 18 F-FDG PET-CT examinations for patients with breast cancer

OBJECTIVES

Determine if subspecialist second opinion review alters reporting of malignancy on 18 F-FDG PET/CT for patients with breast cancer.

METHODS

This IRB-approved retrospective study compared 248 s opinion reads of 18 F-FDG PET/CT exams performed for patients with breast cancer against the original outside institution reports. Subspecialist reviews documented if malignant findings on the outside report were believed to be malignant and noted additional malignant findings not described on the outside report. Reference standard for malignancy or benignity was determined by pathology or follow-up imaging.

RESULTS

Of 248 cases, 27 (11%) had discrepancies in the presence or absence of extra-axillary nodal or distant metastases. Of these 27, 14 (52%) had biopsy or imaging follow-up as a reference standard for malignancy/benignity. In cases with reference standard proof, the subspecialist second opinion review was correct in 13/14 (93%) of cases. This included eleven cases that the original report called malignant, but the subspecialist review called benign and subsequently proven to be benign; as well as two metastases called on subspecialist review, but not on the original report, and subsequently biopsy proven to be metastases. In one case, the second opinion read called a suspicious lesion that was biopsy proven to be benign.

CONCLUSION

Subspecialist review improves the accuracy of diagnosis for the presence or absence of malignancy on FDG PET/CT examinations in patients with breast cancer. This demonstrates the value of performing second opinion reads of 18 F-FDG PET/CT studies in patients with breast cancer, particularly by subspecialist second opinion review reducing false positive reads.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Navigating patient journey in early diagnosis of lung cancer in India

Lung cancer (LC) is one of the leading causes of cancer deaths worldwide. In India, the incidence of LC is increasing rapidly, and a majority of the patients are diagnosed at advanced stages of the disease when treatment is less likely to be effective. Recent therapeutic developments have significantly improved survival outcomes in patients with LC. Prompt specialist referral remains critical for early diagnosis for improved patient survival. In the Indian scenario, distinguishing LC from benign and endemic medical conditions such as tuberculosis can pose a challenge. Hence, awareness regarding the red flags-signs and symptoms that warrant further investigations and referral-is vital. This review is an effort toward encouraging general physicians to maintain a high index of clinical suspicion for those at risk of developing LC and assisting them in refering patients with concerning symptoms to specialists or multidisciplinary teams as early as possible.

Investigation of small lung lesion detection for lung cancer screening in low dose FDG PET imaging by deep neural networks

Purpose

FDG PET imaging is often recommended for the diagnosis of pulmonary nodules after indeterminate low dose CT lung cancer screening. Lowering FDG injecting is desirable for PET imaging. In this work, we aimed to investigate the performance of a deep learning framework in the automatic diagnoses of pulmonary nodules at different count levels of PET imaging.

Materials and methods

Twenty patients with 18F-FDG-avid pulmonary nodules were included and divided into independent training (60%), validation (20%), and test (20%) subsets. We trained a convolutional neural network (ResNet-50) on original DICOM images and used ImageNet pre-trained weight to fine-tune the model. Simulated low-dose PET images at the 9 count levels (20 × 10⁶, 15 × 10⁶, 10 × 10⁶, 7.5 × 10⁶, 5 × 10⁶, 2 × 10⁶, 1 × 10⁶, 0.5 × 10⁶, and 0.25 × 10⁶ counts) were obtained by randomly discarding events in the PET list mode data for each subject. For the test dataset with 4 patients at the 9 count levels, 3,307 and 3,384 image patches were produced for lesion and background, respectively. The receiver-operator characteristic (ROC) curve of the proposed model under the different count levels with different lesion size groups were assessed and the areas under the ROC curve (AUC) were compared.

Results

The AUC values were >0.98 for all count levels except for 0.5 and 0.25 million true counts (0.975 (CL 95%, 0.953-0.992) and 0.963 (CL 95%, 0.941-0.982), respectively). The AUC values were 0.941(CL 95%, 0.923-0.956), 0.993(CL 95%, 0.990-0.996) and 0.998(CL 95%, 0.996-0.999) for different groups of lesion size with effective diameter (R) <10 mm, 10-20 mm, and >20 mm, respectively. The count limit for achieving high AUC (≥0.96) for lesions with size R < 10 mm and R > 10 mm were 2 million (equivalent to an effective dose of 0.08 mSv) and 0.25 million true counts (equivalent to an effective dose of 0.01 mSv), respectively.

Conclusion

All of the above results suggest that the proposed deep learning based method may detect small lesions <10 mm at an effective radiation dose <0.1 mSv.

Advances in knowledge

We investigated the advantages and limitations of a fully automated lung cancer detection method based on deep learning models for data with different lesion sizes and different count levels, and gave guidance for clinical application.

A novel diagnostic model for differentiation of lung metastasis from primary lung cancer in patients with colorectal cancer

Objective

This study aimed to evaluate the ¹⁸F-FDG PET/CT in differentiating lung metastasis(LM) from primary lung cancer(LC) in patients with colorectal cancer (CRC).

Methods

A total of 120 CRC patients (80 male, 40 female) who underwent ¹⁸F-FDG PET/CT were included. The diagnosis of primary lung cancer or lung metastasis was based on histopathology The patients were divided into a training cohort and a validation cohort randomized 1:1. Independent risk factors were extracted through the clinical information and ¹⁸F-FDG PET/CT imaging characteristics of patients in the validation cohort, and then a diagnostic model was constructed and a nomograms was made. ROC curve, calibration curve, cutoff, sensitivity, specificity, and accuracy were used to evaluate the prediction performance of the diagnostic model.

Results

One hundred and twenty Indeterminate lung lesions (ILLs) (77 lung metastasis, 43 primary lung cancer) were analyzed. No significant difference in clinical characteristics and imaging features between the training and the validation cohorts (P > 0. 05). Using uni-/multivariate analysis, pleural tags and contour were identified as independent predictors. These independent predictors were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation cohorts, respectively. The accuracy rate of the diagnostic model for differentiating LM from LC were higher than that of subjective diagnosis (P < 0.05).

Conclusions

Pleural tags and contour were identified as independent predictors. The diagnostic model of ILLs in patients with CRC could help differentiate between LM and LC.

18F-FDG distribution pattern improves the diagnostic accuracy of single pulmonary solid nodule

Background

The main purpose is to explore the use of visual assessment of the heterogeneous distribution of ¹⁸F-FDG in single pulmonary solid lesions to differentiate the benign from the malignant.

Methods

The 200 cases of pulmonary nodules or masses examined by ¹⁸F-FDG PET/CT were retrospectively analyzed. The heterogeneity of ¹⁸F-FDG distribution of the lesion was visually and quantitatively evaluated and the higher part of metabolism was observed and measured at the proximal or distal part to determine the lesion nature. The sensitivity, specificity, PPV, NPV, and accuracy of this method were calculated.

Results

Total 171 pulmonary lesions showed heterogeneity of ¹⁸F-FDG uptake, including the 111 malignant and 60 benign. 54/60 (90.00%) benign lesions showed higher ¹⁸F-FDG uptake visually at distal part, while 104/111 (93.69%) malignant lesions showed higher ¹⁸F-FDG uptake visually at the proximal part. This visual method has good repeatability with a high kappa value (0.821, p<0.001). 52/60 (86.67%) benign lesions showed higher ¹⁸F-FDG uptake quantitatively at distal part, while 107/111 (96.40%) malignant lesions showed higher ¹⁸F-FDG uptake quantitatively at the proximal part. The sensitivity, specificity, PPV, NPV and accuracy of visual and quantitative methods were 93.69%; 96.40%, 90.0%; 86.67%, 94.55%; 93.04%, 88.52%; 92.86%, 92.40%; 92.98%, respectively (p<0.001). When combining the metabolic value and morphological characteristics of PET/CT with visual¹⁸F-FDG heterogeneous features, the accuracy reached to 98.25%. The other 29 lesions (14.5%) with no heterogeneity were smaller (2.17 ± 1.06 vs 3.58 ± 1.48, P<0.001).

Conclusions

Benign and malignant lung lesions showed different heterogeneity of ¹⁸F-FDG uptake. Lung cancer can be effectively distinguished from infectious or inflammatory lesions by this simple and convenient method.

Clinical Applications of Artificial Intelligence in Positron Emission Tomography of Lung Cancer

The ability of a computer to perform tasks normally requiring human intelligence or artificial intelligence (AI) is not new. However, until recently, practical applications in medical imaging were limited, especially in the clinic. With advances in theory, microelectronic circuits, and computer architecture as well as our ability to acquire and access large amounts of data, AI is becoming increasingly ubiquitous in medical imaging. Of particular interest to our community, radiomics tries to identify imaging features of specific pathology that can represent, for example, the texture or shape of a region in the image. This is conducted based on a review of mathematical patterns and pattern combinations. The difficulty is often finding sufficient data to span the spectrum of disease heterogeneity because many features change with pathology as well as over time and, among other issues, data acquisition is expensive. Although we are currently in the early days of the practical application of AI to medical imaging, research is ongoing to integrate imaging, molecular pathobiology, genetic make-up, and clinical manifestations to classify patients into subgroups for the purpose of precision medicine, or in other words, predicting a priori treatment response and outcome. Lung cancer is a functionally and morphologically heterogeneous disease. Positron emission tomography (PET) is an imaging technique with an important role in the precision medicine of patients with lung cancer that helps predict early response to therapy and guides the selection of appropriate treatment. Although still in its infancy, early results suggest that the use of AI in PET of lung cancer has promise for the detection, segmentation, and characterization of disease as well as for outcome prediction.

Update on Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones.CONCLUSIONThis study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.FUNDINGThe National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.

Characterization of 18F-fluorodeoxyglucose metabolic spatial distribution improves the differential diagnosis of indeterminate pulmonary nodules and masses with high fluorodeoxyglucose uptake

Background

The aim of this study was to investigate the value of visual assessment of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) metabolic spatial distribution (V-FMSD) in the diagnosis of indeterminate pulmonary nodules and masses with high ¹⁸F-FDG uptake.

Methods

A total of 301 patients with indeterminate pulmonary nodules or masses who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) imaging were retrospectively studied. The characteristics of ¹⁸F-FDG metabolic spatial distribution (FMSD) in the proximal and distal regions of the lesions were visually analyzed using a 5-point scoring system. The sensitivity, specificity, accuracy, and area under receiver operating characteristic curve (AUC) were compared between V-FMSD and conventional PET/CT methods for the diagnosis of hypermetabolic indeterminate pulmonary nodules and masses.

Results

The V-FMSD results showed that 180 (92.8%) malignant lesions had a score of ≥3 and 78 (72.9%) benign lesions had a score of ≤2. This indicated that the FMSD in the proximal region of malignant lesions was significantly higher than that of the distal region, and the FMSD in the proximal region of benign lesions was significantly lower than that of the distal region. V-FMSD had a specificity of 72.9%, which was markedly higher than those of the maximum standard uptake value (SUV_max; 0%, P<0.001) and the retention index (RI; 26.2%, P<0.001). The AUC of V-FMSD was 0.886, which was significantly larger than those of the SUV_max (0.626, P<0.001), RI (0.670, P<0.001), and PET/CT (0.788, P<0.05).

Conclusions

Our study found that pulmonary benign and malignant lesions have distinct FMSD characteristics. V-FMSD can therefore be used as a novel auxiliary marker to improve the diagnostic accuracy of hypermetabolic indeterminate pulmonary nodules and masses.

Diagnostic accuracy of visual analysis versus dual time-point imaging with 18F-FDG PET/CT for the characterization of indeterminate pulmonary nodules with low uptake

OBJECTIVE

To determine the accuracy of visual analysis and the retention index (RI) with dual-time point ¹⁸F-FDG PET/CT for the characterization of indeterminate pulmonary nodules (IPN) with low FDG uptake.

MATERIALS AND METHODS

A retrospective analysis was performed on 43 patients (28 men, 64 ± 11 years old, range 36-83 years) referred for IPN characterization with ¹⁸F-FDG-PET/CT and maximum standard uptake value ≤ 2.5 at 60 minutes post-injection (SUV_max1). Nodules were analyzed by size, visual score for FDG uptake on standard (OSEM 2,8) and high definition (HD) reconstructions, SUV_max1, SUV_max at 180 minutes post-injection (SUV_max2), and RI was calculated. The definitive diagnosis was based on histopathological confirmation (n = 28) or ≥ 2 years of follow-up.

RESULTS

Twenty-four (56%) nodules were malignant. RI ≥ 10% on standard reconstruction detected 18 nodules that would have been considered negative using the standard SUV_max ≥ 2.5 criterion for malignancy. RI ≥ 10% had a sensitivity, specificity, PPV, NPV and accuracy of 75, 73.7, 78.3, 70, and 74.4%, respectively, while for FDG uptake > liver on HD these were 79.1, 63.2, 73.1, 70.6, and 72.1%, respectively. SUV_max1 ≥ 2, SUV_max2 > 2.5 and FDG uptake > liver on standard reconstruction had a PPV of 100%. FDG uptake > mediastinum on HD had a NPV of 100%.

CONCLUSIONS

RI ≥ 10% was the most accurate criterion for malignancy, followed by FDG uptake > liver on HD reconstruction. On standard reconstruction, SUV_max1 ≥2 was highly predictive of malignancy, as well as SUV_max2 > 2.5 and FDG uptake > liver. FDG uptake < mediastinum on HD was highly predictive of benign nodules.

AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients

BACKGROUND

[¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.

METHODS

One hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.

RESULTS

The AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R² = 0.74). Bias was 42 g and 95% limits of agreement ranged from - 736 to 819 g. Agreement was particularly high in smaller lesions.

CONCLUSIONS

The AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.

Pitfalls and value of organ specific approach in evaluating indeterminate lesions detected on CT in colorectal cancer by [F18] FDG PET/CT

Objective

The objective of this study is to evaluate the value of FDG PET/CT for different involved organs showing Indeterminate/ equivocal / suspicious lesions detected on IV contrasted CT during surveillance follow up for colorectal cancer.

Materials and methods

A total of 67 patients with colorectal cancer how are on regular surveillance follow up by IV contrasted CT scans revealing indeterminate lesions were studied. Subsequent FDG PET/CT evaluation was performed as a problem solving modality. PET/CT results were statistically characterized when compared to biopsy results or to follow/up results. Also Statistical parameters were calculated for each organ involved. The evaluation of all CT indeterminate lesions by FDG PET/CT showed overall sensitivity of 93%, Specificity of 81%, Negative predictive value of 94%, Positive predictive value 80% and accuracy of 87%. However in an organ specific approach the highest accuracy was for lymph nodes with results showing a 100% accuracy and the lowest accuracy was for local disease at a value of 80%. Probable explanations for the falsely characterized lesions resulting in the pitfalls seen and in the imperfect accuracy were provided.

Conclusion

Study shows that FDG PET/CT is an excellent tool in characterizing CT indeterminate lesions during surveillance of colorectal cancer, However different organs showed variable accuracy results with the highest accuracy for our study was for lymph node status (100%) and the lowest accuracy being for local disease at the original site of primary tumor (80%).

Surgery and invasive diagnostic procedures for benign disease are rare in a large low-dose computed tomography lung cancer screening program

OBJECTIVE

Lung cancer screening with low-dose chest computed tomography improves survival. However, concerns about overdiagnosis and unnecessary interventions persist. We reviewed our lung cancer screening program to determine the rate of surgery and invasive procedures for nonmalignant disease.

METHODS

We reviewed all patients undergoing lung cancer screening from January 2012 to June 2017 with follow-up through January 2019. Patients with suspicious findings (Lung CT Screening Reporting and Data System 4) were referred for further evaluation.

RESULTS

Of 3280 patients screened, 345 (10.5%) had Lung CT Screening Reporting and Data System 4 findings. A total of 311 patients had complete follow-up, of whom 93 (29.9%) were diagnosed with lung cancer. Eighty-three patients underwent lung surgery (2.5% of screened patients). Forty patients underwent lobectomy (48.2%), 3 patients (3.6%) underwent bilobectomy, and 40 patients (48.2%) underwent sublobar resection. Fourteen patients underwent surgery for benign disease (0.43% of screened patients). Fifty-four patients, 5 with benign disease, had at least 1 invasive diagnostic procedure but never underwent surgery. The incidence of any invasive intervention for nonmalignant disease was 0.95% (31/3280 patients). There were no postprocedural deaths within 60 days. Twenty-five patients (0.76%) underwent stereotactic body radiation therapy; 19 patients (76%) had presumed lung cancer without pretreatment pathologic confirmation.

CONCLUSIONS

Surgical resection for benign disease occurred in 0.43% of patients undergoing lung cancer screening. The combined incidence of any invasive diagnostic or therapeutic intervention, including surgical resection, for benign disease was only 0.95%. Periprocedural complications were rare. These results indicate that concern over unnecessary interventions is overstated and should not hinder adoption of lung cancer screening. A multidisciplinary team approach, including thoracic surgeons, is critical to maintain an appropriate rate of interventions in lung cancer screening.

[Clinical-radiological-pathological Characteristics of 297 Cases of Surgical Pathology Confirmed Benign Pulmonary Lesions in Which Malignancy Could Not Be Excluded in Preoperative Assessment: A Retrospective Cohort Analysis in a Single Chinese Hospital]

背景与目的

随着癌症早筛意识的提高，低剂量计算机断层扫描(low-dose computed tomography, LDCT)用于肺癌筛查在中国广泛开展。尽管有部分胸部LDCT筛查所见的肺部病灶是肿瘤病灶，但大多数的肺部结节是良性病变。如何有效的对肺部病灶进行术前鉴别，如何降低部分可避免手术的良性疾病的手术切除比例，是需要关注的问题。

方法

本研究纳入2017年1月1日-2018年12月31日期间北京协和医院诊治的，术前考虑肺部恶性病变不能除外，经手术病理确认为良性病变的患者，回顾性分析患者临床信息。

结果

297例患者纳入本研究，占我院肺部病灶行肺部手术治疗患者的9.8%。197例(66.3%)患者因体检行LDCT筛查发现肺部病灶。肺部病变胸部CT影像学评估情况，可评估的323个病灶，平均长径为(17.9±12.1)mm，直径≥8 mm的占91.0%，实性最多见(212/323, 65.6%)，此类肺部病灶可有毛刺征(71/323, 22.0%)、分叶征(94/323, 29.1%)、胸膜牵拉征(81/323, 25.1%)、血管集束征(130/323, 40.2%)、空泡征(23/323, 7.1%)等，提示恶性病变的影像学特征。292例(98.3%)行电视辅助胸腔镜手术(video-assisted thoracoscopic surgery, VATS)，232例(78.1%)患者行肺楔形切除术，13例(4.4%)行肺段切除术，51例(17.2%)患者行肺叶切除术。4例(1.3%)患者出现手术并发症。术后病理类型前3位的是感染性疾病98例(33.0%)、炎性结节96例(32.3%)和错构瘤64例(21.5%)。

结论

因术前不能排除恶性而行手术切除的肺部良性病灶，影像学表现以实性病灶多见，但多具有提示恶性的影像学特征。VATS可作为一种明确病原病理的重要活检方式。此类病灶病理结果以感染性疾病和炎性结节最为常见，错构瘤第三。

Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis

OBJECTIVES

To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis.

METHODS

A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets.

RESULTS

Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively.

CONCLUSION

The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm).

ADVANCES IN KNOWLEDGE

Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.

Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer

Low-dose CT has shown promise in detecting early stage lung cancer. However, concerns about the adverse health effects of radiation and high cost prevent its use as a population-wide screening tool. Effective and feasible screening methods to triage suspicious patients to CT are needed. We investigated human lung cancer metabolomics from 93 paired tissue-serum samples with magnetic resonance spectroscopy and identified tissue and serum metabolomic markers that can differentiate cancer types and stages. Most interestingly, we identified serum metabolomic profiles that can predict patient overall survival for all cases (p = 0.0076), and more importantly for Stage I cases alone (n = 58, p = 0.0100), a prediction which is significant for treatment strategies but currently cannot be achieved by any clinical method. Prolonged survival is associated with relative overexpression of glutamine, valine, and glycine, and relative suppression of glutamate and lipids in serum.

Cost-effectiveness of second-line diagnostic investigations in patients included in the DANTE trial: a randomized controlled trial of lung cancer screening with low-dose computed tomography

AIM

The aim of this study was to analyze the economic efficiency of second-line diagnostic investigations in patients with undetermined lung nodules.

PARTICIPANTS AND METHODS

A retrospective review of all surgical cases included in the DANTE trial from 2001 to 2006 for lung cancer screening was performed. Overall, 217 patients and 261 lung nodules were analyzed. The cohort was divided into patients investigated with PET and/or computed tomography (CT)-guided biopsy (PET-CTB protocol; N=100), compared with those assessed with serial low-dose CT scans (standard protocol; N=161). Outpatient's and inpatient's costs were expressed in euros and derived from the Italian National Health Service. Ineffective costs were defined as the cost of procedures that lead to avoidable surgical intervention.

RESULTS

The diagnostic accuracy of the two protocols was 91% for the standard (sensitivity 100%, specificity 91%, positive predictive value 26%, and negative predictive value 100%) and 90% for the PET-CTB protocol (sensitivity 98%, specificity 81%, positive predictive value 85%, and negative predictive value 97%). Average costs for outpatient's diagnostics were 694 and 1.462 euros, respectively, for the standard and PET-CTB protocol. Average inpatient's costs for both protocols were 12.121 euros. The two protocols showed comparable effectiveness in terms of outpatient's costs (94 and 90%, respectively; P=0.252). Inpatient's costs were effective in 36% of cases monitored according to the standard protocol compared with 85% of patients investigated with PET-CTB protocol. Ineffective costs corresponded to 64 and 15%, respectively (P<0.0001).

CONCLUSION

Despite a higher average cost for outpatient's diagnostics, the implementation of PET imaging with or without CT-guided needle biopsy in the workup of suspicious lung nodules results in reduced unnecessary harm and costs related to inpatient's procedures.

An assessment of 18 F-FDG PET/CT for thoracic screening and risk stratification of pulmonary nodules in multiple endocrine neoplasia type 1

CONTEXT

Bronchopulmonary neuroendocrine tumours (bpNETs) and thymic carcinoid (ThC) are features of multiple endocrine neoplasia type 1 (MEN 1), and surveillance guidelines recommend periodic thoracic imaging. The optimal thoracic imaging modality and screening frequency remain uncertain as does the prognosis of small lung nodules when identified.

OBJECTIVES

To evaluate fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸ F-FDG PET/CT) for identification and prognostic assessment of thoracic lesions in MEN 1.

DESIGN

Retrospective observational study.

SETTING AND PARTICIPANTS

Fifty consecutive MEN 1 patients undergoing screening with ¹⁸ F-FDG PET/CT at a tertiary referral hospital between July 2011 and December 2016.

INTERVENTIONS

¹⁸ F-FDG PET/CT.

OUTCOME MEASURES

Pulmonary and thymic lesion prevalence, size, functional characteristics and behaviour.

RESULTS

Thirteen patients (26.0%) exhibited pulmonary nodules with multiple nodules identified in nine (18.0%). An asymptomatic 31 mm FDG-avid ThC was identified in one patient (2%). Of the 13 patients with pulmonary nodules, four (8.0%) exhibited 13 FDG-avid nodules (mean size 10.1 ± 9.1 mm), and nine (18.0%) demonstrated 26 FDG nonavid nodules (mean size 6.9 ± 5.8 mm). All FDG-avid lesions increased in size vs 11 (42.3%) FDG nonavid lesions (P = .0004). For FDG-avid and nonavid nodules, the median doubling time was 24.2 months (IQR 11.4-40.7) and 48.6 months (IQR 37.0-72.2), respectively. Nodule resection was undertaken in two patients, typical bronchial carcinoid diagnosed in one (FDG nonavid) and metastatic renal cell carcinoma in the second (FDG avid).

CONCLUSION

Thoracic imaging with ¹⁸ F-FDG PET/CT effectively identifies pulmonary nodules and ThC. FDG-avid pulmonary lesions are significantly more likely to progress than nonavid lesions.

Comparison of Bayesian penalized likelihood reconstruction versus OS-EM for characterization of small pulmonary nodules in oncologic PET/CT

OBJECTIVE

To determine whether the recently introduced Bayesian penalized likelihood PET reconstruction (Q.Clear) increases the visual conspicuity and SUV_max of small pulmonary nodules near the PET resolution limit, relative to ordered subset expectation maximization (OS-EM).

METHODS

In this institutional review board-approved and HIPAA-compliant study, 29 FDG PET/CT scans performed on a five-ring GE Discovery IQ were retrospectively selected for pulmonary nodules described in the radiologist's report as "too small to characterize", or small lung nodules in patients at high risk for lung cancer. Thirty-two pulmonary nodules were assessed, with mean CT diameter of 8 mm (range 2-18). PET images were reconstructed with OS-EM and Q.Clear with noise penalty strength β values of 150, 250, and 350. Lesion visual conspicuity was scored by three readers on a 3-point scale, and lesion SUV_max and background liver and blood pool SUV_mean and SUV_stdev were recorded. Comparison was made by linear mixed model with modified Bonferroni post hoc testing; significance cutoff was p < 0.05.

RESULTS

Q.Clear improved lesion visual conspicuity compared to OS-EM at β = 150 (p < 0.01), but not 250 or 350. Lesion SUV_max was increased compared to OS-EM at β = 150 and 250 (p < 0.01), but not 350.

CONCLUSION

In a cohort of small pulmonary nodules with size near an 8 mm PET full-width half maximum, Q.Clear significantly increased lesion visual conspicuity and SUV_max compared to our standard non- time-of-flight OS-EM reconstruction, but only with low noise penalization. Q.Clear with β = 150 may be advantageous when evaluation of small pulmonary nodules is of primary concern.

Value of second-opinion review of outside institution PET-CT examinations

OBJECTIVE

This study aimed to determine whether second-opinion reviews of PET/CT examinations by subspecialists alter reporting of malignant findings.

MATERIALS AND METHODS

This IRB-approved study compared 240 fluorine-18 fluorodeoxyglucose PET/CT consecutively dictated reports by two nuclear medicine subspecialists against the original outside institution reports. Subspecialist reviews documented whether malignant findings on the outside report were malignant and noted additional malignant findings not described on the outside report. The final diagnosis of malignancy or benignity was determined by pathology when available, otherwise by imaging follow-up.

RESULTS

A total of 22 findings (in 20 reports) called suspicious/malignant on the outside reports were deemed benign by subspecialist review. A final diagnosis was available for 20 of 22 findings by pathology (n=3) or follow-up imaging (n=17). The subspecialist review was accurate in 20 (100%) of 20 cases where a final diagnosis was available. The subspecialist review called 11 findings (in 11 reports) suspicious/malignant that were not described or deemed benign on the outside reports. Definitive diagnosis was available for 10 of 11 findings by pathology (n=7) or follow-up imaging (n=3). The second-opinion report was accurate in seven (70%) of 10 cases where a final diagnosis was available.

CONCLUSION

In 31 (13%) of 240 fluorine-18 fluorodeoxyglucose PET/CT examinations performed at an outside institution, subspecialist review resulted in at least one discordant opinion of malignancy. For 28 discrepant cases where a final diagnosis was available, the subspecialist review defined malignancy or benignity correctly in 25 (89%) of 28 cases. This provides evidence for the cost and effort invested in performing second-opinion reviews of PET/CT studies.

Application of Multimodality Imaging Fusion Technology in Diagnosis and Treatment of Malignant Tumors under the Precision Medicine Plan

Objective:

The arrival of precision medicine plan brings new opportunities and challenges for patients undergoing precision diagnosis and treatment of malignant tumors. With the development of medical imaging, information on different modality imaging can be integrated and comprehensively analyzed by imaging fusion system. This review aimed to update the application of multimodality imaging fusion technology in the precise diagnosis and treatment of malignant tumors under the precision medicine plan. We introduced several multimodality imaging fusion technologies and their application to the diagnosis and treatment of malignant tumors in clinical practice.

Date Sources:

The data cited in this review were obtained mainly from the PubMed database from 1996 to 2016, using the keywords of “precision medicine”, “fusion imaging”, “multimodality”, and “tumor diagnosis and treatment”.

Study Selection:

Original articles, clinical practice, reviews, and other relevant literatures published in English were reviewed. Papers focusing on precision medicine, fusion imaging, multimodality, and tumor diagnosis and treatment were selected. Duplicated papers were excluded.

Results:

Multimodality imaging fusion technology plays an important role in tumor diagnosis and treatment under the precision medicine plan, such as accurate location, qualitative diagnosis, tumor staging, treatment plan design, and real-time intraoperative monitoring. Multimodality imaging fusion systems could provide more imaging information of tumors from different dimensions and angles, thereby offing strong technical support for the implementation of precision oncology.

Conclusion:

Under the precision medicine plan, personalized treatment of tumors is a distinct possibility. We believe that multimodality imaging fusion technology will find an increasingly wide application in clinical practice.

Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine allows tailoring of preventive or therapeutic interventions to avoid the expense and toxicity of futile treatment given to those who will not respond. Lung cancer is a heterogeneous disease functionally and morphologically. PET is a sensitive molecular imaging technique with a major role in the precision medicine algorithm of patients with lung cancer. It contributes to the precision medicine of lung neoplasia by interrogating tumor heterogeneity throughout the body. It provides anatomofunctional insight during diagnosis, staging, and restaging of the disease. It is a biomarker of tumoral heterogeneity that helps direct selection of the most appropriate treatment, the prediction of early response to cytotoxic and cytostatic therapies, and is a prognostic biomarker in patients with lung cancer.